LLM in Life Sciences News Tracker

Drug Discovery Platform

April 10, 2026

UVA School of Medicine: YuelDesign AI Diffusion Suite Designs Drug Molecules for Flexible Protein Targets

UVA researchers published a suite of AI tools — YuelDesign, YuelPocket, and YuelBond — across PNAS, JCIM, and Science Advances. The centrepiece, YuelDesign, uses diffusion models to design drug molecules for protein pockets that flex and shift shape during binding — unlike most existing methods that treat targets as rigid structures. YuelPocket uses graph neural networks to identify binding sites (including on AlphaFold-predicted structures), while YuelBond validates chemical bond accuracy. Tested on CDK2 (cancer), YuelDesign uniquely captured conformational changes upon drug binding. All tools released as open-source. DOI: 10.1126/sciadv.aeb7045, 10.1073/pnas.2524913123 Science Advances | UVA Health

Drug Discovery Platform Partnership

April 6, 2026

Pharmaphorum Roundup: Verily $300M, BMS/insitro ALS Expansion, Latent Labs Agent, Zealand AI Hub

Key AI drug discovery developments compiled: Verily completed a $300M private placement (led by Series X Capital), transitioning from Alphabet majority control to independent Verily Health Inc. — funds accelerate precision health AI platform. BMS expanded insitro ALS collaboration with two new TDP-43 targets (ALS-2, ALS-3) via insitro's Virtual Human platform, triggering $10M payment; insitro advancing its own antisense oligonucleotide programme for ALS-1. Latent Labs (ex-DeepMind) launched Latent-Y, an autonomous antibody design agent completing campaigns 56x faster than expert estimates. Zealand Pharma establishing Cambridge, MA AI hub for US headquarters. Source

Clinical Trials Drug Discovery Platform

April 2, 2026

173 AI-Discovered Drug Programs Now in Clinical Development — Phase III Results Will Define 2026

As of early 2026, over 173 AI-discovered drug programs are in clinical development globally: approximately 94 in Phase I, 56 in Phase II, and 15 in Phase III. Between 15 and 20 programs are expected to enter pivotal trials this year. Phase III remains the critical test — AI-discovered compounds are only now reaching this stage, and results later in 2026 will determine whether the technology delivers clinically validated drugs at scale. Gero signed a research agreement with Chugai Pharmaceutical for AI-discovered therapies targeting age-related diseases. Scripps/Gero AI-identified anti-aging candidates extended lifespan in animal models, with 70%+ compounds showing significant results. Source

Drug Discovery Platform

April 1, 2026

Drug Target Review: AI Drug Discovery Enters Pivotal Year — FDA Guidance, EU AI Act, Market Consolidation

Drug Target Review analysis examines where AI is delivering measurable gains in early discovery and where hype outpaces reality. Market projected to grow from $5–7B (2025) to $8–10B (2026). FDA draft AI guidance expected to be finalised in 2026, requiring credibility assessment plans for high-risk AI applications. EU AI Act high-risk provisions take effect 2 August 2026, potentially classifying some drug development AI as high-risk. Smaller AI drug discovery companies face existential pressures — multiple shutdowns, 20%+ workforce reductions, and delisting despite substantial backing. 50:1 ratio between announced biobucks and actual upfront payments. Fundamental challenge remains data availability, not algorithmic sophistication. Source

Partnership Funding Drug Discovery

March 29, 2026

Eli Lilly and Insilico Medicine Strike $2.75 Billion AI Drug Discovery Deal

Eli Lilly signed a $2.75B deal with Insilico Medicine for exclusive global rights to develop, manufacture, and commercialise preclinical oral drug candidates discovered using Insilico's Pharma.AI generative platform. Insilico receives $115M upfront; remainder tied to development, regulatory, and commercial milestones plus tiered royalties. This is a tenfold escalation from their $100M November 2025 partnership. Insilico pipeline now includes 28 drugs, nearly half at clinical stage. Targets span oncology, metabolic disease, immunology — including a GLP-1 candidate and pan-KRAS inhibitor. Insilico shares jumped 15% on announcement. Sceptics note persistent gaps between in-silico promise and clinical reality, and concerns about opaque training data limiting reproducibility. BioPharma Dive | Bloomberg

Platform Drug Discovery Genomics & Proteomics

March 23, 2026

Latent Labs Launches Latent-Y: First Lab-Validated Autonomous AI Agent for De Novo Antibody Design

Latent Labs (founded by ex-DeepMind AlphaFold2 co-lead Dr Simon Kohl; $50M total funding from Radical Ventures, Sofinnova, Google chief scientist Jeff Dean) launched Latent-Y, an AI agent that autonomously designs therapeutic antibodies from a text prompt — compressing weeks of expert work into hours. Powered by Latent-X2, the agent handles target analysis, epitope selection, candidate design, computational validation, and iteration. In user studies, PhD-level experts completed campaigns 56x faster. Demonstrated across diverse campaign types including cross-species binder design and blood-brain barrier crossing. All design decisions logged with reasoning for scientist review. Latent Labs | BusinessWire

Partnership Drug Discovery Platform

March 23, 2026

BMS and insitro Expand ALS Collaboration — Two Additional TDP-43 Targets via Virtual Human AI Platform

Bristol Myers Squibb nominated two additional AI-identified ALS targets (ALS-2 and ALS-3) through insitro's Virtual Human platform, expanding the six-year collaboration with a $10M payment. These join ALS-1 (nominated December 2024). insitro is advancing its own antisense oligonucleotide for ALS-1 while simultaneously progressing a small molecule candidate for BMS. The collaboration focuses on processes modulating TDP-43 mislocalisation — a central disease mechanism in nearly 97% of ALS patients. Virtual Human platform applies machine learning, human genetics, and functional genomics to generate predictive in vitro models. Source

Platform Genomics & Proteomics Drug Discovery

March 20, 2026

Nature Biotechnology Review: Generalist Biological AI (GBAI) — Modelling the Language of Life

A major review in Nature Biotechnology synthesises rapid advances in biological AI to interpret and generate DNA, RNA, proteins, and cellular systems. The paper charts a course toward comprehensive generalist systems performing multiple critical biological tasks simultaneously. Key opportunities: synergising language and structural AI, leveraging specialised models, and improving AI agents for autonomous discovery. GBAI could deepen understanding of disease pathways and biomarkers, advance automated therapeutic design, and integrate within virtual cells to simulate biological activity. Significant barriers remain in data availability, biological complexity, scalability, and experimental validation. DOI: 10.1038/s41587-026-03064-w Source

Drug Discovery Genomics & Proteomics Platform

March 17, 2026

Nature Biotechnology: AI Turbocharges Antibody Discovery — AIntibody Challenge Benchmarks 29 Organisations

Nature Biotechnology feature reports on AI-assisted antibody discovery, including results from the AIntibody Challenge with 29 participating organisations (pharma, startups, academics). David Baker's lab published the first peer-reviewed description of de novo antibody design from scratch (Nature, November 2025). Xaira ($1B founding, six Baker lab co-founders) developing AI-predicted antibody binders with drug-like properties for human studies. Key challenge: most industry AI models remain hidden, limiting collective progress. Experts call for open benchmarking and shared failure data. DOI: 10.1038/s41587-026-03048-w Source

Genomics & Proteomics Platform

March 17, 2026

Nature Biotechnology: Genome Editing's Third Act — Mutation-Agnostic Therapies Enter the Clinic

Nature Biotechnology reports the newest gene editors are shifting from tackling mutations one by one toward universal, mutation-agnostic therapies. Tessera Therapeutics received US and Australian regulatory clearance for its gene writer therapy clinical trials, backed by a $150M Regeneron partnership. Alltrna plans IND filing in 2026 for engineered transfer RNA correcting shared premature stop codons across multiple diseases. ARPA-H launched Rare Disease AI/ML for Precision Integrated Diagnosis programme. California Institute for Regenerative Medicine committed $100M to RAPID (Rare-disease Acceleration through Platform Innovation and Delivery). David Liu (Broad Institute): strategy is to develop genome-based therapeutics applicable beyond single specific mutations. DOI: 10.1038/s41587-026-03058-8 Source

Platform Genomics/Proteomics

Mar 26, 2026

Nature Communications: RNA Base Pairing Rules Learned with Only 21 Parameters

Researchers demonstrated that the fundamental biological rules of RNA base pairing can be learned from sequences alone — with no structural training data — using a model with only 21 parameters. This challenges the dominant assumption that deep learning in biology requires massive parameter spaces and extensive training data. The work shows that biologically grounded inductive biases can dramatically reduce model complexity while maintaining predictive accuracy, with implications for RNA structure prediction and therapeutic RNA design where training data remains scarce. Source

Drug Discovery Platform

Mar 26, 2026

Bio-IT World: AI Running 10-20 Million Predictions/Day in Drug Discovery — but What's a Prediction Worth?

Expert Systems CEO Tudor Oprea reports generative AI is now running an estimated 10-20 million predictions per day across the pharmaceutical industry to explore new molecules and reactions. However, Oprea argues a critical question is being ignored: the economic value of each prediction. His position is that a trustworthy prediction should be worth at least 5% of the actual experiment it replaces. LLM outputs still hallucinate and cannot be fully trusted, though reasoning-specialised LLMs perform better on complex multi-step problems. AstraZeneca's PIP platform alone makes ~1 million predictions daily. Active learning coupled with agentic AI gives GPU-rich companies the highest probability of success. Source

Clinical Trials Platform

Mar 25, 2026

Nature Communications: TrialMatchAI — Open-Source LLM System for Clinical Trial Patient Matching

Researchers published TrialMatchAI, an end-to-end AI recommendation system that automates patient-to-trial matching using fine-tuned open-source LLMs within a retrieval-augmented generation (RAG) framework. The system normalises biomedical entities, retrieves trials using hybrid lexical/semantic search, and performs criterion-level eligibility assessment via medical chain-of-thought reasoning. In real-world oncology validation, 92% of patients had at least one relevant trial in the top 20 results; expert assessment validated >90% accuracy in eligibility classification, particularly for biomarker-driven matches. Open-source and deployable locally for privacy. DOI: 10.1038/s41467-026-70509-w. Source

Platform Genomics/Proteomics

Mar 2026

Nature Communications: CellScope — Tree-Structured Framework Reveals Multi-Level Single-Cell Hierarchies

Li and colleagues introduced CellScope, a tree-structured computational framework that reveals multi-level cellular hierarchies and gene functions from single-cell RNA-seq data. Unlike flat clustering approaches, CellScope provides hierarchical organisation that mirrors biological tissue structure, with intuitive visualisation and deep biological views into cell types and their functional relationships. The approach addresses a key limitation of existing single-cell analysis: most methods produce flat clusterings that obscure the nested, hierarchical nature of cell populations in tissues. Source

Drug Discovery Platform

Mar 2026

Nature Communications: 3D Molecular Foundation Model Predicts Protein and Small Molecule Properties

Researchers published a 3D molecular foundation model trained across diverse biological domains that accurately predicts properties of both proteins and small molecules from a unified architecture. The model aids in the discovery of potential antiviral compounds, demonstrating cross-domain transfer learning from general molecular representation to specific therapeutic applications. This represents a shift from separate protein and small-molecule models toward unified molecular representations — a prerequisite for modelling the full drug-target interaction landscape computationally. Source

Drug Discovery Platform

Mar 2026

Nature Communications: CAMPER — Mechanistic AI Platform Designs Antimicrobial Peptides Effective Against MRSA

Researchers introduced CAMPER, a mechanistic artificial intelligence platform for designing antimicrobial peptides targeting methicillin-resistant Staphylococcus aureus (MRSA). The platform identified a stable peptide that eradicates MRSA biofilms and persister cells — two of the most treatment-resistant bacterial states — and demonstrated activity in mouse infection models. The work addresses the critical antimicrobial resistance (AMR) crisis by demonstrating that AI-designed peptides can overcome resistance mechanisms that defeat conventional antibiotics. Source

Platform Genomics/Proteomics Drug Discovery

Mar 2026

Nature Communications: GenSLM Protein Language Model Designs Functional Enzymes Outperforming Natural Variants

Researchers demonstrated that using the GenSLM protein language model to design TrpB enzyme variants yields stable, active enzymes with broad substrate promiscuity, outperforming both natural and laboratory-evolved counterparts. The study addresses a fundamental bottleneck in enzyme engineering: identifying functional starting points for optimisation. By generating diverse, functional enzyme variants computationally, the approach accelerates biocatalyst development for pharmaceutical synthesis and industrial biotechnology applications. Source

Drug Discovery Platform

Mar 2026

Nature Communications: M2UMol — Multimodal Knowledge Transfer for Drug Discovery with Missing Data

Researchers introduced M2UMol, a framework that transfers multimodal molecular knowledge (3D structure, spectral data, bioactivity profiles) into 2D molecular representations, enabling accurate property predictions even when experimental modalities are missing. Drug discovery often suffers from incomplete multimodal data — a compound may have structure data but lack spectral or binding data. M2UMol addresses this by distilling cross-modal knowledge during training, then making predictions from whatever data is available at inference time. Source

Platform Genomics/Proteomics

Mar 20, 2026

Nature Communications: UCASpatial — Ultra-Precision Deconvolution for Spatial Transcriptomics

Xu and colleagues published UCASpatial, an entropy-weighted algorithm that enables ultra-precision deconvolution of spatial transcriptomics data. The method resolves fine-grained immune cell landscapes and explores intercellular communication mechanisms at a resolution not achievable with existing approaches. Accurately mapping diverse cell types in complex tissues remains a major challenge for spatial transcriptomics; UCASpatial's entropy-weighting approach addresses this by adaptively handling varying confidence levels across spatial spots. Source

Platform Partnership Drug Discovery

Mar 19, 2026

GEN Edge: GTC 2026 Signals Agentic AI Inflection in Healthcare — 70% Adoption, 5,000+ Startups

GEN Edge analysis of NVIDIA GTC 2026 reports that healthcare AI has reached an inflection point: 70% of healthcare organisations now actively adopt AI (up from 63% in 2024), 69% use generative AI/LLMs (up from 54%), and NVIDIA's Inception programme has grown to 5,000+ healthcare/life sciences startups. NVIDIA VP Kimberly Powell declared "the transformer moment is now for biology." Healthcare AI startup ecosystem captured >85% of sector AI spending last year. The $4.9 trillion healthcare industry is deploying AI at more than twice the rate of the broader economy. Pharmaceutical leaders Roche and Lilly are investing in AI infrastructure at unprecedented levels. Source

Drug Discovery Platform

Mar 20, 2026

Frontiers in Bioinformatics: HAMGNN Graph Neural Network for LLM-Enhanced Drug Repurposing

Researchers published HAMGNN, a heterogeneous attention-based meta-learning graph neural network that integrates LLM-extracted therapeutic knowledge into a biomedical knowledge graph (DrugBank, DisGeNET, Hetionet; 2.2M+ edges). The model uses relation-sensitive multi-head attention and disease-focused meta-learning for rapid adaptation to unseen diseases. HAMGNN achieved ROC-AUC of 0.98 and precision of 0.95 on cold-start disease generalisation, representing 10-15% improvement over TxGNN and GAT-GNN baselines. DOI: 10.3389/fbinf.2026.1755412. Source

Platform Partnership Drug Discovery

Mar 18, 2026

NVIDIA GTC 2026: Healthcare AI Stack Expands with nvQSP, BioNeMo Genomics Blueprints

At GTC 2026, NVIDIA detailed expanded healthcare AI capabilities. nvQSP, a GPU-accelerated quantitative systems pharmacology simulation engine, achieves 77x speedup over traditional methods for testing treatment scenarios before clinical trials. New BioNeMo Blueprints for single-cell analysis and genomics were released. Basecamp Research is using BioNeMo/Parabricks for its Trillion Gene Atlas; Tahoe Therapeutics trains virtual cell models on single-cell data at scale; PerturbAI applies the platform to a large in-vivo CRISPR functional genomics atlas. 70% of healthcare organisations now actively adopt AI, up from 63% in 2024. Source

Platform Partnership

Mar 16, 2026

Roche Deploys Pharma's Largest Hybrid-Cloud AI Factory with 3,500+ NVIDIA GPUs

Roche announced expansion of its AI infrastructure with 2,176 NVIDIA Blackwell GPUs on-premises across the US and Europe, bringing its total to 3,500+ GPUs — the largest announced hybrid-cloud AI factory in the pharmaceutical industry. The infrastructure supports Genentech's "Lab-in-the-Loop" strategy using NVIDIA BioNeMo for drug discovery, Omniverse digital twins for manufacturing (including a new GLP-1 facility in North Carolina), and Parabricks for diagnostics. Builds on a 2023 Genentech-NVIDIA research collaboration. Announcement made at NVIDIA GTC 2026. Source

Platform Drug Discovery Genomics/Proteomics

Mar 16, 2026

NVIDIA GTC: Proteina-Complexa Model + AlphaFold DB Expansion with 30M Protein Complexes

At GTC 2026, NVIDIA launched Proteina-Complexa, a generative model for protein binder design as part of the BioNeMo platform. Novo Nordisk, Viva Biotech and Manifold Bio are early adopters, designing and experimentally validating generated protein binders. Separately, NVIDIA, Google DeepMind, EMBL-EBI and Seoul National University expanded the AlphaFold Protein Structure Database with ~30 million new AI-predicted protein complex structures and 1.7 million high-confidence predictions. This significantly broadens the resource for structure-based drug discovery. Source

Platform Genomics/Proteomics

Mar 16, 2026

Nature Communications: CellVQ Foundation Model for Single-Cell Transcriptomics

Researchers introduced CellVQ, a 500-million parameter single-cell foundation model trained on 68 million cells. Key innovation: a Single-Cell Discretisation (SCD) module that transforms high-dimensional sparse single-cell data into a compact "cell code," addressing data heterogeneity and improving interpretability. CellVQ-Graph extends the model by integrating cell features with multimodal data (genes, cell communication, annotations) into a knowledge graph for biological discovery. Addresses key limitations of existing single-cell foundation models around data sparsity and interpretability. DOI: 10.1038/s41467-026-70071-5. Source

Platform Clinical Trials

Mar 16, 2026

npj Digital Medicine: Comprehensive Review of AI-Driven Virtual Cell Models for Preclinical Research

A comprehensive review in npj Digital Medicine maps the landscape of AI-driven virtual cell models — computational models simulating cellular functional states, signalling networks, and dynamics under diverse perturbations. The review covers technical pathways (single-cell RNA-seq, spatial transcriptomics, proteomics), validation mechanisms, and clinical translation potential. Highlights terminological fragmentation across "virtual cell," "digital cell," and "digital twin" as an obstacle to cross-disciplinary communication and regulation. Calls for ISO-style standardisation and explainable-AI integration to improve clinical acceptability. DOI: 10.1038/s41746-025-02198-6. Source

Drug Discovery Platform

Mar 2026

AAAI 2026: CLADD — Multi-Agent LLM Framework for Drug Discovery Outperforms Domain-Specific Models

Published in AAAI 2026 proceedings, CLADD (Collaborative LLM Agents for Drug Discovery) from Genentech Research introduces a RAG-empowered multi-agent system using general-purpose LLMs for drug discovery. The framework includes specialised Planning, Knowledge Graph and Molecule Understanding teams that dynamically retrieve from biomedical knowledge bases without domain-specific fine-tuning. CLADD outperforms both general-purpose and domain-specific LLMs, as well as traditional deep learning approaches, on tasks including drug-target prediction, toxicity classification and molecular captioning. Code publicly available. Source

Clinical Trials Platform

Mar 14, 2026

Nature Communications: Federated Learning Enables Precision Oncology for Rare Cancers (atomCAT)

The international atomCAT consortium published federated multivariable Cox models trained across 14 centres (1,428 patients) and externally validated at 2 additional centres (277 patients) for anal cancer prognosis. The approach achieves consistent calibration and discrimination (c-indices 0.68-0.79) without centralising sensitive patient data. Identifies T stage, nodal involvement, tumour volume, sex and chemotherapy regimen as key prognostic factors. Demonstrates federated learning as a viable path for precision oncology in rare cancers where centralised data collection is impractical. DOI: 10.1038/s41467-026-70297-3. Source

Platform Genomics/Proteomics

Mar 14, 2026

Nature Communications: EPInformer — Scalable Deep Learning for Enhancer-Gene Expression Prediction

Researchers from Harvard/MIT published EPInformer, a deep learning framework that integrates promoter-enhancer sequences, epigenomic signals and chromatin contacts to predict gene expression. With only 0.4 million parameters, EPInformer outperforms existing methods while providing interpretable enhancer-gene interactions. The lightweight architecture makes it practical for genome-wide screening of regulatory variants, addressing a key bottleneck in connecting non-coding genetic variation to disease mechanisms. Complements larger models like AlphaGenome with a more targeted, interpretable approach. Source

Clinical Trials Platform Genomics/Proteomics

Mar 10, 2026

Nature Communications: TRIM — TCR-RNA Model Predicts Immunotherapy Response from Pre-Treatment Blood

Researchers developed TRIM (TCR-RNA Integrating Model), which jointly analyses T cell receptor sequences and single-cell RNA profiles to predict intra-tumour T cell signatures following checkpoint inhibitor treatment. Critically, the model works from pre-treatment blood or tissue samples, enabling prospective patient stratification before immunotherapy begins. This addresses a major unmet need in precision immuno-oncology: identifying which patients will respond to expensive checkpoint inhibitors before treatment starts, rather than after. Source

Funding Drug Discovery

Mar 13, 2026

Earendil Labs Considers Hong Kong IPO for AI Drug Discovery

Bloomberg reports AI drug discovery startup Earendil Labs is considering a Hong Kong listing that could raise up to $500 million. The company is working with China International Capital Corp. (CICC) and Morgan Stanley on the potential share sale. The move follows Insilico Medicine's successful December 2025 HK IPO, which raised $293M and was massively oversubscribed. Earendil's consideration signals continued investor appetite for AI-first drug discovery platforms in Asian capital markets. Source

Drug Discovery Platform

Mar 12, 2026

Nature: Machine Learning Predicts Drug Enantioselectivity from Sparse Data

UCLA and University of Utah researchers published in Nature a machine learning system that predicts how molecules form during drug synthesis. The method, trained on sparse data, cuts months of lab work to days. Lead author Prof. Abigail Doyle notes the tool is "highly applicable" for optimizing reactions in drug development phases. The transferable enantioselectivity model addresses a key challenge in pharmaceutical chemistry where small molecular changes can dramatically affect drug efficacy and safety. DOI: 10.1038/s41586-026-10239-7. Source

Platform Genomics/Proteomics

Mar 10, 2026

Nature Communications: MINT Protein Language Model for Protein-Protein Interactions

Researchers introduced MINT, the first protein language model (PLM) specifically trained on protein-protein interactions using the STRING database of 96 million interactions. MINT outperforms existing PLMs (including ESM-2) in binding affinity prediction and mutational effect estimation. The model excels at modeling complex protein assemblies, antibody-antigen interactions, and T-cell receptor-epitope binding. Key innovation: adapting model architecture to handle multiple protein sequences simultaneously while maintaining scalability. Source

Platform Genomics/Proteomics Drug Discovery

Mar 10, 2026

Nature Communications: PPLM Achieves State-of-the-Art Protein Interaction Prediction

Zhang Lab published the Protein Pair Language Model (PPLM), which jointly encodes paired protein sequences to learn interaction-aware representations. PPLM achieves state-of-the-art performance on cross-species protein-protein interaction prediction (human, mouse, fly, worm, E. coli, yeast). PPLM-Affinity outperforms both ESM2 and structure-based methods on binding affinity modeling, including challenging antibody-antigen and TCR-pMHC complexes. The approach advances therapeutic discovery by improving target-drug interaction predictions. Source

Clinical Trials Drug Discovery Regulatory

Mar 10, 2026

AI Drug Discovery Status: 200+ Programs in Clinical Trials, Phase III Readouts Imminent

Comprehensive analysis confirms over 200 AI-discovered drugs now in clinical development, with 15-20 entering pivotal Phase III trials in 2026. Insilico's rentosertib Phase IIa results (published Nature Medicine): 98.4mL FVC improvement (60mg) vs -62.3mL placebo decline over 12 weeks. FDA final AI guidance expected Q2 2026. First AI-discovered drug approval probability: ~60% by late 2026 or early 2027. Key watchpoints: Takeda's zasocitinib (TYK2 inhibitor) Phase III in psoriasis, Recursion's REC-4881 registrational path, and Generate Biomedicines' GB-0895 Phase III in asthma. Source

Platform Partnership Drug Discovery

Mar 8, 2026

Pharma AI Platform Licensing Emerges as New Business Model

GEN analysis: 2026 marks shift from single-asset AI deals to platform licensing. Noetik-GSK: $50M upfront, 5-year subscription for foundation models predicting cancer clinical outcomes—described as "first true foundation model licensing deal in biotech." Chai-Lilly: Platform deployment for biologics design across multiple targets. Boltz-Pfizer: Small molecule drug discovery. Generate Biomedicines CEO Mike Nally: typical 10-15 year discovery-to-approval journey could compress to 8 years with AI platforms. Pattern suggests pharma investing in AI infrastructure, not just molecules. Source

Clinical Trials Platform

Mar 2026

Medable Launches Agent Studio: First Agentic AI for Clinical Development

BioMed Nexus reports Medable launched Agent Studio, described as the industry's first agentic AI platform for clinical development. The platform automates routine clinical trial processes and reduces unproductive "white space" caused by manual workflows. Medable's end-to-end system integrates eCOA, eConsent, remote data collection, telemedicine, and connected device management. Partners include retail pharmacy networks, home health providers, and connected sensor companies. Part of broader trend of AI moving into clinical operations beyond drug discovery. Source

Genomics/Proteomics Platform

Mar 2026

Nature Biotechnology: GRAPE-LM Enables One-Round RNA Aptamer Evolution

Nature Biotechnology published GRAPE-LM (Generator of RNA Aptamers Powered by activity-guided Evolution and Language Model), a generative AI framework enabling one-round generation of short RNA binders. When guided by CRISPR-Cas-based intracellular screening, GRAPE-LM outperforms traditional multi-round aptamer evolution methods. The approach combines generative AI with single wet lab evolution round to generate high-affinity RNA aptamers, significantly accelerating therapeutic RNA development. DOI: 10.1038/s41587-026-03008-4. Source

Funding Partnership Drug Discovery

Jan 4, 2026

Insilico Medicine Signs $888M Servier Cancer Partnership Post-IPO

Less than a week after its Hong Kong IPO, Insilico Medicine announced an oncology discovery and development partnership with French pharma Servier valued at up to $888 million. Deal includes $32 million in upfront and R&D-related payments. Focused on "challenging targets" in cancer research using Insilico's Pharma.AI platform. Insilico leads AI-driven discovery; Servier shares R&D expenses and leads clinical validation. Part of ongoing validation of AI drug discovery platforms attracting major pharma investment. Source

Funding Drug Discovery Platform

Dec 30, 2025

Insilico Medicine Completes $293M Hong Kong IPO—Largest AI Biotech Listing

Insilico Medicine listed on Hong Kong Stock Exchange, becoming first AI-driven biotech on HKEX Main Board under Chapter 8.05 rules. Raised HKD 2.277 billion ($293M)—largest biotech IPO in Hong Kong 2025. Public offering oversubscribed 1,427x, locking HKD 328B+ in subscription funds. Cornerstone investors: Eli Lilly, Tencent, Temasek, Schroders, UBS AM, Oaktree. Lead candidate rentosertib (TNIK inhibitor) in Phase IIb/III for IPF. Post-IPO allocation: 48% clinical R&D, 20% early discovery, 15% AI model development, 12% automated lab expansion. Source

Clinical Trials Platform Drug Discovery

Mar 6, 2026

Recursion Pharmaceuticals Achieves First AI Clinical Proof-of-Concept

Recursion Pharmaceuticals reported Q4 earnings marking a key milestone: first AI-enabled clinical proof-of-concept. Phase 2 data for REC-4881 in familial adenomatous polyposis showed 43% median polyp reduction at 4mg once-daily dose, with 75% of patients responding. Responses remained durable after three-month treatment pause. Company engaging FDA on registrational path targeting 2026. Over $500M in cumulative partnership milestones. Platform now houses 50+ petabytes of multimodal data across phenomics, transcriptomics, proteomics, ADME, and 300M+ real-world patient lives. AI-driven discovery engine synthesizes only 300-330 compounds per program vs ~2,500 for industry peers. Source

Platform Drug Discovery Partnership

Mar 3, 2026

Insilico Medicine + Liquid AI Release LFM2-2.6B-MMAI Foundation Model

Insilico Medicine and Liquid AI announced partnership creating lightweight scientific foundation models for pharmaceutical research. LFM2-2.6B-MMAI (v0.2.1) is a single 2.6B-parameter model performing at state-of-the-art levels across drug discovery subdomains. Outperformed TxGemma-27B (10x larger) on 13/22 pharmacokinetics and toxicology tasks. Achieved 98.8% success rate on molecular optimization benchmarks. Trained on ~120B tokens across 200+ tasks. Operates entirely on private infrastructure, addressing pharma's data security concerns. Part of Insilico's Pharmaceutical Superintelligence (PSI) roadmap. Source

Drug Discovery Regulatory

Mar 1, 2026

Wiley Review Confirms No AI-Only Drug Has Achieved FDA Approval

Comprehensive peer-reviewed analysis published in Drug Development Research confirms that despite more than a decade of intensive research, no AI-only originated drug has achieved full regulatory approval. AI-guided molecules have progressed into clinical trials with encouraging early-phase success rates. Key limitations identified: poor data quality and accessibility, lack of model interpretability, gaps between computational predictions and chemical feasibility, and inherent complexity of biological systems. AI remains a supportive tool rather than standalone solution, underscoring continued need for human expertise and experimental validation. Source

Platform Drug Discovery Infrastructure

Feb 23, 2026

2026 AI Power Shift: Biotech Industry Enters "Builder Phase"

Drug Discovery News analysis based on Benchling 2026 Biotech AI Report: industry has entered "builder" phase where successful organizations are reshaping data environments and organizational structures to make AI a default operating model. 80% of organizations plan to increase AI budgets in next 12 months; 23% expect to double spend. 50% report faster time-to-target; 42% see accuracy uplift. Predictive models lead adoption: protein structure prediction (73%), docking models (52%). Poor data quality cited as #1 reason AI pilots fail (55% of organizations). Capital moving into data infrastructure and scientific modeling capabilities. Source

Infrastructure Platform

Feb 23, 2026

Ardigen Report: 95% of Enterprise GenAI Pilots Failed

Ardigen's AI in Biotech 2026 trends report cites MIT 2025 study finding nearly 95% of enterprise generative AI pilots failed to deliver measurable business impact. Failures occurred because systems remained disconnected from real workflows, data foundations, and organizational ownership. Shift from algorithms to data infrastructure: next phase of AI in biotech will be defined less by new algorithms and more by whether organizations can move from experimentation to dependable infrastructure. US AI biotech market approximately $2.1B in 2025, with growth driven by drug discovery, genomics, and precision medicine. Platform-oriented strategies dominating (e.g., Lilly TuneLab, Ginkgo Datapoints). Source

Infrastructure

Feb 18, 2026

AI Slashing Jobs Across Industries—But Pharma May Be Spared

PharmaVoice analysis indicates that despite a wave of AI-fueled layoffs across industries, pharma and biotech could be spared from massive job losses for now. AI integration requires talent to run and understand the technology. Companies that demonstrate willingness to adopt AI tools depends on risk appetite and having budget and skills to create AI solutions in-house or source externally. AI particularly helps early-stage companies where AI-derived trial improvements are more prominent. Integration can be tougher for bigger companies with legacy systems. Source

Platform Drug Discovery

Feb 17, 2026

University of Missouri Releases PSBench: 1.4 Million Protein Models

University of Missouri researchers released PSBench, the world's largest collection of protein models with quality assessment—1.4 million annotated protein structure models verified by independent experts. Database gives scientists reliable information to build more accurate AI systems for assessing protein structure model quality, critical for developing medical treatments for Alzheimer's, cancer, and other diseases. Led by Prof. Jianlin "Jack" Cheng, a Curators' Distinguished Professor in Bioinformatics. Even AlphaFold has limitations—no single AI tool is consistently accurate for every protein type, making quality benchmarks essential. Source

Regulatory Clinical Trials

Feb 2026

Clinical Trials 2026: AI Fluency Becomes #1 Organizational Differentiator

Applied Clinical Trials Online outlines sharp divide emerging in 2026: organizations building AI fluency into every layer of clinical process vs. legacy operators still piloting standalone "AI use cases." AI fluency—measured in talent, governance, and operational agility—will dictate survival. Key trends: living protocols with machine-readable design auto-created from biomedical concept libraries; secondary data utilization unlocking billions in latent insights; trial workforce transformation with demand for clinical data product managers, digital trial architects, and AI governance leads. FDA wants traceable, explainable logic and robust data provenance for clinical decisions. Source

Regulatory Platform

Feb 2026

FDA Deploys Agentic AI Capabilities Across Agency

FDA announced deployment of agentic AI capabilities for all agency employees (December 2025), launching two-month Agentic AI Challenge for staff to build and demonstrate solutions. Follows June 2025 launch of Elsa, an agency-wide generative AI assistant for scientific reviewers and investigators. January 2026: FDA published "Guiding Principles of Good AI Practice in Drug Development." CDER has seen significant increase in drug applications using AI components across nonclinical, clinical, postmarketing, and manufacturing phases. CDER AI Council established in 2024 to coordinate AI activities and ensure consistent external communications. Source

Clinical Trials Drug Discovery

Feb 2026

PitchBook: AI-Native Biotechs Show 80-90% Phase I Success Rate

PitchBook analysis (via BioSpace) indicates AI-native biotechs—companies using AI as foundational technology—have achieved approximately 80-90% Phase I success rate, compared to industry average of 40-65%. Phase II success rate dropped to 40%, still above industry average of 29%. Caveat: AI-focused biotechs have completed only ~10 clinical trials so far, making dataset nascent. Higher Phase I success rates may reflect improved target selection. AI enables biotechs to produce more "shots on goal" without escalating costs—structural de-risking. Smaller companies can now compete with big pharma on trial efficiency. Source

Funding Drug Discovery Platform

Feb 26, 2026

Generate Biomedicines Raises $400M in Largest Biotech IPO of 2026

Flagship Pioneering-backed Generate Biomedicines raised $400M in the year's largest biotech IPO, trading on Nasdaq as GENB. Lead candidate GB-0895, an AI-designed anti-TSLP monoclonal antibody, is in Phase 3 for severe asthma with twice-yearly dosing (vs. monthly competitors). The company previously demonstrated computer-to-clinic speed of 17 months for an earlier COVID candidate. Board includes Nobel laureate Frances Arnold and Moderna CEO Stéphane Bancel. Fifth biotech IPO in February 2026, bringing total monthly proceeds to ~$1.4B. Source

Infrastructure Platform Drug Discovery

Feb 26, 2026

Eli Lilly AI Factory Goes Live with Industry's Most Powerful Pharma Supercomputer

Eli Lilly activated its AI factory, the pharmaceutical industry's most powerful AI supercomputer built on NVIDIA DGX SuperPOD with DGX B300 systems. The "LillyPod" platform enables scientists to build chatbots, agentic workflows, and research lab agents. Select models will be available through Lilly TuneLab via federated learning infrastructure built on NVIDIA FLARE. Supports the $1B co-innovation lab with NVIDIA announced in January 2026. Chief AI Officer Thomas Fuchs: "This machine is exactly how AI should be used—for science, to lessen suffering and improve the human condition." Source

Platform Drug Discovery

Feb 24, 2026

Benchling 2026 Biotech AI Report: Industry Enters "Builder Phase"

Benchling's 2026 Biotech AI Report based on 100 biotech organizations reveals AI "killer apps": literature review (76% adoption), protein structure prediction (71%), scientific reporting (66%), and target identification (58%). Half of organizations report faster time-to-target; 80% plan to increase AI budgets in next 12 months. Data quality and availability cited as #1 reason AI pilots fail. 66% of scientists report increased LLM confidence but maintain "trust but verify" approach. Industry shifting from task copilots to integrated discovery systems. Source

Autonomous AI Drug Discovery Platform

Feb 24, 2026

LUMI-Lab Self-Driving Laboratory Discovers Novel mRNA Delivery Materials

University of Toronto researchers published LUMI-lab (Large-scale Unsupervised Modeling followed by Iterative experiments) in Cell, integrating molecular foundation models pretrained on 28M+ molecular structures with robotic systems. The SDL screened 1,700+ lipid nanoparticles across ten active-learning cycles, unexpectedly discovering brominated-tail ionizable lipids that enhance mRNA delivery to human lung cells—a class not previously linked to mRNA delivery. Results outperformed FDA-approved benchmarks with favorable safety profiles. Source

Infrastructure Clinical Trials

Feb 24, 2026

Jeeva Clinical Trials: Infrastructure, Not AI, Is the Bottleneck

Maryland-based Jeeva Clinical Trials amplified a key message from JPMorgan Healthcare Conference and BIO International: AI won't transform drug development without infrastructure evolution. CEO Harsha Rajasimha: "AI is not the constraint. The constraint is infrastructure. If you deploy advanced intelligence on siloed, outdated systems, you amplify inefficiency. If you deploy AI on a unified, cloud-native architecture, you amplify speed, compliance, and patient impact." Companies modernizing digital infrastructure today will define the next decade of clinical research. Source

Platform Drug Discovery

Feb 19, 2026

Isomorphic Labs Launches IsoDDE "AlphaFold 4" Drug Design Engine

DeepMind spinoff Isomorphic Labs released IsoDDE (Isomorphic Labs Drug Design Engine), described by Columbia's Mohammed AlQuraishi as "a major advance on the scale of an AlphaFold 4." IsoDDE more than doubles AlphaFold 3 accuracy on protein-ligand predictions, predicts binding affinities faster than gold-standard physics-based methods (FEP+), and identifies cryptic binding pockets from sequence alone—including sites that took researchers 15+ years to discover experimentally (cereblon). Unlike AlphaFold, IsoDDE is proprietary with limited technical disclosure. Already deployed in Novartis, Eli Lilly, and Johnson & Johnson partnerships. Source

Partnership Drug Discovery Platform

Feb 18, 2026

Merck and Mayo Clinic Announce AI Drug Discovery Partnership

Merck and Mayo Clinic announced an R&D agreement applying AI, advanced analytics, and multimodal clinical data to drug discovery—Mayo's first collaboration of this scale with a global biopharma company. Merck gains access to Mayo Clinic Platform_Orchestrate, enabling AI models to run inside Mayo's secure environment with de-identified clinical, genomic, imaging, and molecular data. Merck SVP Greg Hersch emphasized Mayo's "unique wealth of de-identified clinical, molecular multimodal data sets" not readily available elsewhere in clean, curated form. Initial focus: inflammatory bowel disease, atopic dermatitis, multiple sclerosis. Source

Platform Drug Discovery

Feb 16, 2026

MIT LLM Predicts Optimal Codons for Protein Drug Manufacturing

MIT researchers developed an LLM that analyzes the genetic code of industrial yeast Komagataella phaffii—specifically the codons it uses—to predict which codons work best for manufacturing therapeutic proteins. The model boosted production efficiency of six proteins including human growth hormone and monoclonal antibodies used to treat cancer. "Having predictive tools that consistently work well is really important to help shorten the time from having an idea to getting it into production. Taking away uncertainty ultimately saves time and money." Source

Drug Discovery Regulatory

Feb 16, 2026

AI in Drug Discovery 2026 Predictions: First FDA Approval Expected 2027-2028

Drug Target Review analysis notes no AI-discovered drug has achieved FDA approval as of December 2025, with first approval expected 2027-2028. Chinese AI drug discovery companies increased share of global biotech licensing deals from 21% (2023-2024) to 32% (Q1 2025). Market consolidation accelerating: multiple companies shut down despite substantial backing, others announced 20%+ workforce reductions, several pursued delisting. Valuations collapsed since 2021-2022 IPO peaks. 50:1 ratio between announced "biobucks" and actual upfront payments reveals industry caution. Source

Platform Drug Discovery

Feb 10, 2026

Benchling AI Now Generally Available with 500+ Biotech Companies

Benchling AI reached general availability after deployment across 500+ biotech companies from AI-native startups to top-20 pharma. Features AI agents for scientific tasks including Ask, Compose, Deep Research, and Data Entry. Integrates scientific models (AlphaFold 2, Chai-1, Boltz-2) directly in workflows. At Beam Therapeutics, regulatory writers use Deep Research for document preparation. In one large organization, hundreds of scientists adopted it within weeks to unlock years of captured data. Compose is being used to migrate 20,000 legacy ELN entries into structured, searchable formats. Source

Platform Autonomous AI

Nov 5, 2025

Google Launches Deep Research with Gmail and Drive Integration

Google announced that its Deep Research feature, available to all Gemini users, can now access emails and private documents from Gmail, Drive, and Chat to provide better answers. The autonomous "deep browse" capability creates comprehensive reports by pulling information directly from users' documents, email threads, and project plans alongside web sources. Source

Platform Drug Discovery

Nov 4, 2025

Recursion Pharmaceuticals Achieves $30M Milestone from Roche/Genentech

Recursion received a $30 million milestone from Roche/Genentech for delivering a whole-genome map of microglial immune cells, bringing cumulative partner payments to over $500 million. The company announced leadership transition with CEO Chris Gibson stepping down January 2026, replaced by Najat Khan, while reporting $785M cash runway through 2027. Source

Platform Autonomous AI

Nov 4, 2025

Cambridge Researchers Launch Denario: AI Assistant for Complete Scientific Process

Trinity Hall Cambridge announced the development of Denario, an AI-powered scientific assistant designed to accelerate the scientific process by helping researchers identify new research questions, analyze and interpret data, and produce scientific documents covering every step from hypothesis generation to final publication. Source

Platform Autonomous AI

Oct 30, 2025

Google Unveils Gemini 2.0 AI Co-Scientist for Scientific Discovery

Google Research announced a multi-agent AI system built with Gemini 2.0 as a virtual scientific collaborator. The system uses test-time compute scaling to iteratively reason and improve outputs through self-play scientific debates for hypothesis generation and ranking tournaments. Domain experts testing 15 open research goals found the AI co-scientist outperformed other state-of-the-art agentic models, with some AI-generated hypotheses already validated experimentally. Source

Infrastructure Drug Discovery

Oct 29, 2025

Lilly Deploys World's Largest AI Factory for Drug Discovery

Eli Lilly unveiled the world's largest AI factory wholly owned by a pharmaceutical company, featuring 1,016 NVIDIA Blackwell Ultra GPUs in a DGX SuperPOD system. This AI factory will train large-scale biomedical foundation models for drug discovery and development, with select models available through Lilly TuneLab platform for the broader biotech ecosystem. Source

Partnership Infrastructure

Oct 28, 2025

Microsoft-OpenAI Partnership Restructured with $250B Azure Commitment

Microsoft and OpenAI reached a new deal allowing OpenAI to restructure into a public benefit corporation while securing a $250 billion Azure cloud services contract. The agreement maintains Microsoft's frontier model partnership while allowing OpenAI more independence, including ability to release open-weight models and develop products with third parties. Source

Platform Drug Discovery

Oct 28, 2025

Harbour BioMed Launches World's First Fully Human Generative AI Antibody Model

Unveiled at Global R&D Day in Shanghai, Harbour BioMed introduced its first fully human Generative AI HCAb Model powered by the Hu-mAtrIx™ AI platform. The closed-loop system achieved 78.5% success rate with 107 de novo generated binder sequences, with validated molecules showing nanomolar-level binding affinity and average yields exceeding 700 mg/L. Source

Platform Autonomous AI

Oct 27, 2025

OpenAI Targets Autonomous AI Researcher by March 2028

OpenAI CEO Sam Altman revealed ambitious internal timelines to create an AI research intern by September 2026 and a fully autonomous AI researcher by March 2028. Chief Scientist Jakub Pachocki described this as a "system capable of autonomously delivering on larger research projects," with current models already matching top human performers. The announcement coincided with OpenAI's restructuring to a public benefit corporation, with the non-profit foundation committing $25 billion to use AI for curing diseases. Source

Partnership

Oct 27, 2025

Thermo Fisher Scientific Partners with OpenAI for Drug Development

Thermo Fisher Scientific revealed a strategic collaboration embedding OpenAI APIs into its Accelerator Drug Development platform and PPD clinical research business. Modeling shows 25-60% time savings across trial documentation (10,000-15,000 documents per trial) and site activation processes. Source

Platform Multi-Omics

Oct 27, 2025

World's First Multi-Omics LLM Showcased in Riyadh

BioAro Inc. announced the world's first large language model constructed from multi-omics data, called "The BioIntelligence™," powering their PanOmiQ™ platform. This breakthrough was showcased at the Global Health Exhibition in Riyadh and promises to decode the complete vocabulary of human biology by integrating genomics, transcriptomics, metabolomics, and proteomics data. Source

Platform Autonomous AI

Oct 27, 2025

"A Survey of AI Scientists" Published on arXiv

A comprehensive survey paper on AI scientists was published by researchers from multiple institutions. The paper charts the field's evolution from early Foundational Modules (2022-2023) to integrated Closed-Loop Systems (2024), and finally to the current frontier of Scalability, Impact, and Human-AI Collaboration (2025-present). The survey analyzes how artificial intelligence is transitioning from a computational instrument to an autonomous originator of scientific knowledge. Source

Platform Infrastructure Autonomous AI

Oct 26, 2025

Duke Engineers Build AI-Powered Autonomous Research Microscope

Duke University published research in ACS Nano describing ATOMIC, an AI optical microscope that analyzes 2D materials autonomously. The system doesn't just follow instructions—it understands them, can assess samples, make decisions independently, and produce results as well as a human expert. This represents a significant advancement toward autonomous research where AI systems work alongside humans. Source

Platform Autonomous AI

Oct 22, 2025

Sakana AI's AI Scientist v2 Produces First Peer-Reviewed AI Paper

Sakana AI announced that The AI Scientist v2 successfully produced the first fully AI-generated paper to pass peer review at the ICLR 2025 workshop level. The updated system adds agentic tree search for open-ended idea exploration, vision-language model reviewer capabilities, and parallel execution. The accepted paper documented a "negative result," sparking debate about the quality and role of AI-generated research. Source

Regulatory Drug Discovery

Oct 22, 2025

MHRA Launches AI-Powered Drug Interaction Prediction System

The UK's Medicines and Healthcare products Regulatory Agency announced three AI projects, including a groundbreaking study using artificial intelligence and NHS data to predict side effects from drug combinations before they reach patients. The flagship project, backed by £859,650 in government funding, will analyze anonymized NHS data focusing on cardiovascular medicines. Source

Platform

Oct 20, 2025

Anthropic Launches Claude for Life Sciences Platform

Anthropic unveiled Claude for Life Sciences, marking its formal entry into the life sciences sector. Built on Claude Sonnet 4.5, the platform integrates with Benchling, PubMed, 10x Genomics, and Synapse.org. Early adopters like Sanofi report daily usage among the majority of employees, while Novo Nordisk continues to reduce clinical study report production from 10+ weeks to 10 minutes. Source

Platform Drug Discovery

Oct 17, 2025

Google DeepMind's AI Model Discovers Novel Cancer Immunotherapy Pathway

Google DeepMind and Yale University's Cell2Sentence-Scale 27B (C2S-Scale) foundation model generated and experimentally validated a novel hypothesis for cancer treatment. The 27-billion-parameter AI identified that combining silmitasertib with low-dose interferon increases antigen presentation by 50%, potentially making "cold" tumors visible to the immune system. Source

Partnership Drug Discovery

Oct 15, 2025

Absci and Owkin Partner for Generative AI Drug Discovery

Absci and Owkin formed a strategic partnership to co-develop therapeutic candidates in immuno-oncology, immunology, and inflammation. Owkin's predictive AI models will optimize target selection using extensive biomedical datasets and patient-derived organoids, while Absci's generative AI Drug Creation platform can go from AI-designed antibodies to wet lab-validated candidates in as little as six weeks. Source

Platform Drug Discovery

Oct 9, 2025

AlphaFold 3 Database Major Update and Widespread Adoption

The AlphaFold Database received significant updates. Published analysis in Precision Clinical Medicine documented AlphaFold 3's unprecedented accuracy in predicting drug-like interactions, achieving 50% improvement over traditional methods and 76% accuracy in protein-ligand interactions, accelerating target identification and drug optimization. Source

Partnership Clinical Trials

Oct 2, 2025

Sanofi Ventures Invests in QuantHealth for AI Clinical Trial Simulation

QuantHealth secured strategic investment from Sanofi Ventures to accelerate AI-driven clinical trial simulation and digital twin technologies. The platform enables pharmaceutical companies to virtually simulate trials using over 350 million patient records, predicting trial outcomes and optimizing protocol design to improve success rates and reduce costs. Source

Platform Infrastructure Drug Discovery

Oct 1, 2025

BioNTech AI Day 2025 Showcases Kyber Supercomputer and BFN Protein Models

Held in London, BioNTech and InstaDeep showcased their near-exascale Kyber supercomputer and Bayesian Flow Network (BFN) models for protein sequence generation. The event highlighted AbBFN2, achieving 90% success rate in antibody humanization completed in under 20 minutes while preserving binding affinity, and new InstaNovo model delivering 10-15% accuracy increase in peptide sequencing. Source

Platform Drug Discovery

Aug 14, 2025

MIT Researchers Design Novel Antibiotics Using Generative AI for Superbugs

MIT's Antibiotics-AI Project announced that generative AI designed over 36 million novel compounds, identifying effective candidates against drug-resistant gonorrhoea and MRSA. The compounds work through novel membrane-disruption mechanisms and showed effectiveness in both laboratory and animal studies. Source

Drug Discovery Clinical Trials

Jun 3, 2025

Insilico Medicine's AI-Designed Drug Rentosertib Shows Efficacy in Phase 2a Trial

Published in Nature Medicine, Rentosertib became the first fully AI-discovered and AI-designed drug to demonstrate both safety and preliminary efficacy in human trials. The TNIK inhibitor for idiopathic pulmonary fibrosis showed a mean lung function improvement of +98.4 mL in the 60mg dosage group versus -20.3 mL decline in placebo, compressing target discovery to clinical candidate selection into just 18 months. Source

Platform Multi-Omics

Nov 23, 2025

Protein Set Transformer: Genome Language Model for Viromics

Researchers published Protein Set Transformer (PST), a protein-based genome language model that models complete genomes as sets of proteins without requiring functional labels. The transformer architecture enables high-diversity viral genomics analysis, addressing exponential increases in microbial and viral genomic data. PST helps overcome limitations of homology-based analyses that struggle with rapid viral genome divergence. Source

Infrastructure Regulatory

Nov 22, 2025

UK Launches £600M Health Data Research Service for AI Drug Discovery

The UK Government announced the Health Data Research Service (HDRS) backed by up to £600M from government and Wellcome Trust. HDRS will provide a single access point to large-scale, AI-ready pathology, radiology, and genomic datasets from multiple sources. The infrastructure supports AI model development and validation for biomarker identification, disease modelling, and pre-clinical models to improve drug response prediction. Source

Regulatory Clinical Trials

Nov 21, 2025

UK Plans Radical Clinical Trial Acceleration Using AI

The UK Government unveiled plans to reduce clinical trial set-up times from nine months to ten weeks, part of broader efforts to strengthen the life sciences sector. Chris Meier from Boston Consulting Group emphasized that AI, data, and analytics are critical to shortening timelines and enabling new drug discovery modes. The initiative aims to make UK hospitals more competitive globally for pharmaceutical company trials. Source

Partnership Platform Multi-Omics

Nov 20, 2025

NVIDIA and Sheba Medical Center Build 'ChatGPT for Genomics'

NVIDIA partnered with Sheba Medical Center (Israel) and Mount Sinai Hospital (New York) on a three-year, multi-million dollar project to create large language models trained on biological language. The ambitious goal is to decode the majority of the poorly understood human genome, enabling users to input whole genome sequences and receive personalized health risk assessments and treatment recommendations. Success depends on data quality, interpretability, and validation, with regulatory approval and ethical frameworks remaining uncertain. Source

Platform

Nov 20, 2025

NVIDIA Releases BioCLIP 2 Foundation Model for Biodiversity

NVIDIA launched BioCLIP 2, an NVIDIA-accelerated biology foundation model trained on the largest and most diverse dataset of organisms to date. The model identifies over a million species, demonstrating the potential of foundation models to organize and analyze massive biological datasets. This represents advancement in using AI for biodiversity research and ecological monitoring. Source

Platform Drug Discovery

Nov 20, 2025

MarkerPredict: Machine Learning for Clinically Relevant Biomarkers

Researchers published MarkerPredict, a bio-primed machine learning approach designed to enhance discovery of clinically relevant biomarkers. The methodology addresses challenges in biomarker identification by integrating biological priors with machine learning algorithms, improving the reliability and clinical utility of discovered biomarkers across multiple disease contexts. Source

Platform Drug Discovery Multi-Omics

Nov 18, 2025

Multimodal AI Framework for Drug-Target Interaction Prediction

Scientists introduced the Unified Multimodal Molecule Encoder (UMME) with Adaptive Curriculum-guided Modality Optimization (ACMO) for drug discovery. The framework integrates molecular graphs, protein sequences, and omics profiles while handling missing modality scenarios. The system uses confidence-based ranking and curriculum learning to prioritize reliable data during training, maintaining strong performance even with modality absence or noise, which mimics realistic drug screening conditions. Source

Platform Drug Discovery

Nov 14, 2025

Pistoia Alliance: Multi-Agent LLMs Excel in Database Queries

The Pistoia Alliance completed Phase 1 of its LLMs in Life Sciences project, focusing on target discovery and validation. Key finding: multi-agent LLM systems that challenge each other's outputs and interact with users significantly outperform single models in querying biological databases like Open Targets. The approach offers flexibility without requiring prior knowledge of database structure or query templates. Phase 2 will focus on creating proper benchmarks for natural language data mining systems. Source

Platform Drug Discovery

Nov 10, 2025

MAGE: AI-Designed Antibodies Without Existing Templates

Vanderbilt researchers published in Cell their development of MAGE (Monoclonal Antibody Generator), using protein language models to design functional human antibodies from scratch without needing existing antibody templates. The system was trained on H5N1 avian influenza antibodies and successfully generated antibodies recognizing related but previously unseen influenza strains. Traditional antibody discovery takes months; MAGE could reduce this to hours, dramatically accelerating pandemic response capabilities. In silico predictions require extensive experimental validation. Source

Platform Drug Discovery

Nov 15, 2025

Foundation Models Survey Published in Biology and Chemistry

A comprehensive survey on large language models in biology and chemistry was published, reviewing molecular representation strategies for biological macromolecules and small organic compounds. The review confirms that approximately 30% of published foundation models are now multimodal, with 20% trained specifically on protein sequences. The survey highlights the rapid evolution of protein language models that enable faster structure prediction, drug screening, and protein design without traditional multiple sequence alignment requirements. Source

Partnership Platform

Nov 13, 2025

OpenAI Exploring Consumer Health Applications

OpenAI is considering a significant push into consumer health products, including a personal health assistant and health data aggregator. The company has hired Nate Gross (Doximity co-founder) to lead healthcare strategy and Ashley Alexander (formerly Instagram) as VP of health products. With ChatGPT attracting 800 million active weekly users—many seeking medical advice—investors believe OpenAI could address longstanding challenges in personal health record management that have stymied previous Big Tech attempts. Source

Drug Discovery

Nov 13, 2025

AI in Biotechnology Market Projected to Reach $22.7 Billion by 2035

The AI in Biotechnology Market is projected to reach $22.7 billion by 2035, with rapid growth driven by machine learning integration in drug discovery, personalized medicine, and genomics research. Multiple reports confirm AI will drive 30% of new drug discoveries by 2025, with companies like Pfizer achieving 30-day drug discovery timelines (reduced from years) and saving 16,000 research hours annually. Source

Partnership Drug Discovery

Nov 9, 2025

Insilico Medicine and Eli Lilly Research Collaboration

Insilico Medicine announced a research collaboration with Eli Lilly, combining Insilico's Pharma.AI platform with Lilly's development expertise to discover innovative therapies. The agreement includes over $100 million in potential payments (upfront, milestones, and tiered royalties). This expands their 2023 AI software licensing partnership and validates Insilico's AI-driven drug discovery capabilities, which have nominated 22 preclinical candidates at an average pace of 12-18 months per program—significantly faster than the traditional 2.5-4 year timeline. Source

Platform Drug Discovery

Nov 9, 2025

DeepMind's AlphaFold 3 AI-Designed Drugs to Enter Human Trials

Google DeepMind CEO Demis Hassabis announced that AI-designed drugs using AlphaFold technology will enter human trials this year. AlphaFold 2 has predicted structures for virtually all 200 million identified proteins—work that would have taken a billion years of PhD time at traditional rates. AlphaFold 3's ability to model protein-ligand interactions with 50% greater accuracy than traditional physics-based methods is accelerating drug discovery timelines from years to potentially weeks or months. Source

Platform Drug Discovery

Nov 5, 2025

David Baker Lab: AI Achieves Atomic Precision in Antibody Design

Nobel Laureate David Baker's lab at the University of Washington announced a breakthrough using RFdiffusion AI to design full-length antibodies from scratch with atomic precision (RMSD values as low as 0.3 Å). The technology can design all six complementarity-determining regions de novo, compressing discovery timelines from years to weeks without requiring animal immunization or extensive screening. This could revolutionize the $200 billion antibody drug industry and enable treatments for previously "undruggable" diseases. Source

Platform

Oct 20, 2025

Anthropic Launches Claude for Life Sciences

Anthropic unveiled Claude for Life Sciences, marking its first formal entry into life sciences research. Built on Claude Sonnet 4.5 and optimized for laboratory protocols, the platform integrates with Benchling, PubMed, 10x Genomics, and Synapse.org to streamline R&D processes from literature reviews to regulatory submissions. Anthropic aims for Claude to support "a meaningful percentage of all life science work globally," reducing tasks that previously took days to mere minutes. Source

Partnership Infrastructure Drug Discovery

Sep 10, 2025

Absci Accelerates AI-Driven Drug Discovery with Oracle and AMD

Absci announced a collaboration with Oracle Cloud Infrastructure and AMD to accelerate generative AI-driven drug discovery. Using OCI's AI infrastructure with AMD Instinct MI355X GPUs, Absci has reduced inter-GPU latency to 2.5 µs and achieved terabytes-per-second throughput for large-scale molecular dynamics simulations. The company claims its Integrated Drug Creation Platform cuts drug development timelines by 14 months and costs by 75%, with Phase 1/2a trials for hair regrowth therapy ABS-201 planned for December 2025. Source

Platform

Dec 5, 2025

GenSyntax: Product-Contextualized LLM for Prokaryotic Genomes

Researchers unveiled GenSyntax, a specialized LLM trained on 49,250 annotated prokaryotic genomes for whole-genome decoding. This product-contextualized model advances genomic sequence analysis and functional annotation capabilities for microbial research, demonstrating enhanced performance on prokaryotic genome interpretation tasks. Source

Drug Discovery Platform

Dec 4, 2025

Cambridge Uses GPT-4 as AI Scientist for Drug Combination Discovery

Cambridge University researchers used GPT-4 as an "AI scientist" to identify novel drug combinations for cancer treatment. The study demonstrated that LLMs can explore hypothesis spaces that human researchers might miss due to cognitive biases. Professor Ross King noted the AI predicted combinations "no one would have found apart from randomly trying things," showcasing LLM capabilities beyond pattern recognition. Source

Platform

Dec 4, 2025

Nature Biotechnology Highlights Agentic AI in Lab Automation

Nature Biotechnology's 2025 research review highlighted how agentic AI systems using LLMs are streamlining laboratory workflows, including CRISPR system selection and gene transfer pathway discovery. The journal emphasized that with resources like ToolUniverse, LLM-powered AI co-scientists will increasingly guide hands-on research, moving beyond advisory roles to active experimental design. Source

Platform

Dec 4, 2025

Science Immunology Editorial: Are AI Immunologists Ready?

Science Immunology published a perspective piece examining whether LLMs are ready for immunology research applications. The editorial addresses the "meteoric rise" of LLMs and evaluates their readiness for analyzing immune system data and assisting immunological discovery, while cautioning about current limitations in specialized domains requiring deep mechanistic understanding. Source

Platform Clinical Trials

Dec 3, 2025

Medical-Specific LLM Outperforms GPT-4o on Clinical Tasks

John Snow Labs' medical-specific LLM (under 10 billion parameters) outperformed GPT-4o on clinical tasks when evaluated by medical doctors using the CLEVER framework. The smaller, domain-trained model was preferred 45-92% more often on factuality, clinical relevance, and conciseness, demonstrating that specialized healthcare LLMs can surpass larger general-purpose models through focused training on medical corpora. Source

Regulatory Clinical Trials

Dec 3, 2025

Responsible AI Framework for LLM-Driven Clinical Decision Support

A collaborative framework for responsible AI in LLM-driven clinical decision support systems for precision oncology was published in npj Precision Oncology. The framework addresses ethical implementation, patient data handling, and regulatory alignment using real-world patient data, providing guidelines for healthcare institutions deploying LLM-based diagnostic and treatment recommendation systems. Source

Platform Drug Discovery

Dec 2, 2025

RFdiffusion3 Released for Protein Design

The Institute for Protein Design released RFdiffusion3, an AI foundation model capable of generating proteins that interact with any molecular type. The tool offers 10-fold faster performance over RFdiffusion2 and uses atom-level diffusion for unprecedented precision in designing enzymes, biosensors, and gene therapy tools. This release follows Nobel recognition for protein design advances. Source

Platform

Dec 2, 2025

Biomedical Knowledge Graph Construction Using GPT-4o

Researchers developed IP-RAR (Integrated and Progressive Retrieval-Augmented Reasoning) and constructed BioStrataKG, a stratified knowledge graph from large-scale biomedical articles using GPT-4o mini. The system demonstrates enhanced cross-document question answering and multihop reasoning for biomedical knowledge extraction, published in GigaScience. Source

Platform

Dec 1, 2025

European Commission Releases Largest Human Biology Dataset for AI

The European Commission's Joint Research Centre released the largest dataset of human biology for data-driven AI techniques. This resource, comprising hundreds of thousands of entries, aims to enhance biomedical research through cutting-edge AI and LLM applications, supporting Europe's AI health research infrastructure development. Source

Drug Discovery Platform

Jan 11, 2026

DrugCLIP: 10 Million Times Faster Virtual Drug Screening

Tsinghua and Peking University researchers unveiled DrugCLIP, AI-driven screening enabling virtual screening of 10,000 proteins and 500 million compounds in single day, generating 2 million small-molecule hits. System achieves 10 million-fold speed increase versus current methods by combining contrastive learning and dense retrieval, enabling cross-screening of 10+ trillion protein-molecule pairs. Published in Science (DOI: 10.1126/science.ads9530), platform openly accessible for global drug discovery. Converts protein pockets and molecules into mathematical vectors for rapid matching, validated by computational and laboratory testing. Source

Drug Discovery Infrastructure Partnership

Jan 9, 2026

Zealand Pharma Secures Access to Denmark's Gefion AI Supercomputer

Zealand Pharma entered agreement with DCAI to use Gefion, Denmark's flagship AI supercomputer, for accelerating drug discovery. Access provides unprecedented computational power for large-scale modeling, prediction, and optimization of drug candidates. Agreement supports Zealand's Metabolic Frontier 2030 strategy targeting five launches, 10+ clinical pipeline programs, and industry-leading cycle times from idea to clinic by 2030. Represents European pharmaceutical companies securing sovereign AI computing capabilities for competitive advantage and data sovereignty. Source

Regulatory Clinical Trials Platform

Jan 6, 2026

Utah Launches First State-Approved AI Prescription Renewal Pilot

Utah launched first state-approved artificial intelligence program for autonomous prescription renewals. Twelve-month pilot permits AI to evaluate patient history and clinical data to approve refills for 190 common chronic medications including diabetes and hypertension treatments. Program developed by Doctronic in partnership with Utah's Office of Artificial Intelligence Policy and Department of Commerce. Controlled substances, ADHD medications, and injectables excluded for safety reasons. Represents first regulatory approval for autonomous AI medication management without physician review for each refill. Source

Clinical Trials Platform Regulatory

Jan 2, 2026

Stanford/Harvard Release Major AI Clinical Safety Benchmark

Stanford and Harvard researchers released NOHARM benchmark evaluating 31 large language models on 100 real primary care cases across 10 medical specialties. Study found top-performing AI models make 12-15 severe errors per 100 cases, while worst-performing systems exceed 40 severe errors. Models from Google, OpenAI, Anthropic, Meta, and medical platforms (AMBOSS, Glass Health) were compared against board-certified internal medicine physicians. Two-thirds of American physicians currently use LLMs in clinical practice, with one in five consulting systems for second opinions. Benchmark publicly available at bench.arise-ai.org. Source

Funding Platform Drug Discovery

Dec 30, 2025

Insilico Medicine: First AI-Driven Biotech IPO on Hong Kong Exchange

Insilico Medicine (3696.HK) listed on Hong Kong Stock Exchange, becoming first AI-driven biotech to go public under Main Board Chapter 8.05 rules. IPO raised HKD 2.277 billion ($293M USD), marking largest biotech IPO in Hong Kong for 2025. Public offering oversubscribed 1,427x, locking subscription funds exceeding HKD 328 billion. Shares jumped 25-45% on trading debut. Capital allocation: 48% clinical R&D, 20% early-stage drug discovery, 15% new generative AI models, 12% automated lab expansion. Company holds 300+ peer-reviewed papers, 700+ patents/applications, featured in Nature Index 2024 top 100 global institutions and Nature Biotechnology December 2025 cover. Source

Platform

Dec 24, 2025

Cornell Study: LLMs Transform Scientific Publishing Landscape

Cornell University study published in Science analyzed 2+ million papers across arXiv, bioRxiv, and SSRN (January 2018-June 2024), demonstrating LLM adoption dramatically increased scientific output. Researchers using LLMs posted 33% more papers on arXiv, exceeding 50% increase on bioRxiv and SSRN. Largest benefit for non-native English speakers: Asian-affiliated institutions increased output 43-89% depending on platform. Critical finding: growing AI-written content makes it harder for decision-makers to distinguish meaningful work from low-value content. Study also identified literature search advantage: AI-powered tools surfaced newer papers and relevant books more effectively than traditional search methods. Source

Drug Discovery Platform

Dec 23, 2025

AI-Generated Antibodies Tolerated by Human Immune Cells

Drug Discovery World reported AI-generated antibodies successfully tolerated by human immune cells, representing critical milestone toward clinical applications. Development follows December 9 Nature article on AI-designed antibodies approaching clinical trials, demonstrating rapid progression from computational design to biological validation. Progress indicates accelerating pathway from AI protein design to therapeutic candidates. Source

Regulatory Platform

Dec 19, 2025

AI Transforms Medical Genetics: Comprehensive Review Published

Genes journal published comprehensive review examining AI's integration with medical genetics, highlighting transformative role in diagnostic precision, non-invasive molecular profiling, and predictive medicine. Review emphasizes AI as generative tool in therapeutic design accelerating drug discovery, protein engineering, and precision gene editing, while addressing ethical challenges including data privacy, algorithmic bias, and dual-use biosecurity risks. Source

Funding

Dec 17, 2025

Life Sciences Industry Prioritizes AI Investment for 2026

FTI Consulting survey of 300 US life sciences decision-makers reveals 59% plan increased investment in AI and LLM initiatives for 2026, with R&D receiving 51% focus. Survey indicates 70% optimistic outlook despite fundraising uncertainty, with AI viewed as critical competitive advantage. Nearly 6 in 10 companies increasing AI/LLM budgets signals industry-wide recognition of transformative potential. Source

Platform Clinical Trials

Dec 16, 2025

Mount Sinai V2P: AI Decodes Disease-Causing Genetic Variants

Mount Sinai researchers developed V2P (Variant-to-Phenotype), phenotype-specific AI model published in Nature Communications that connects genetic variants to disease types with improved accuracy. System provides "clearer window" into how genetic changes translate into disease, enabling better prioritization of genes and pathways for therapeutic development. Represents advancement over general variant prediction tools by categorizing disease associations. Source

Platform

Dec 15, 2025

John Snow Labs Medical LLMs Win InfoWorld Technology of the Year

John Snow Labs' Medical LLMs recognized by InfoWorld for industry-leading accuracy and clinical impact. Peer-reviewed research shows models outperform GPT-4.5 and Claude 3.7 by 61-200% in factuality, clinical relevance, and conciseness while operating at fraction of cost. Platform includes 2,500+ pre-trained medical models with HIPAA, NIST AI RMF, and EU AI Act compliance, demonstrating domain-specialized superiority over general-purpose LLMs. Source

Regulatory

Dec 11, 2025

EFPIA Report: AI Governance Challenges in Medicine Lifecycles

European Federation of Pharmaceutical Industries and Associations published report highlighting governance challenges and policy actions needed to implement AI regulation across medicines' lifecycles. Report addresses gap between AI innovation pace and regulatory frameworks, emphasizing need for coordinated approach to ensure safety and efficacy while enabling advancement in pharmaceutical development. Source

Regulatory

Dec 11, 2025

European Commission Publishes Digital Omnibus on AI Regulation

European Commission released Digital Omnibus proposal for AI regulation, analyzed by Member States with criticisms regarding planned delays for key EU AI Act duties. Development represents evolving regulatory landscape attempting to balance innovation with safety requirements in AI deployment across healthcare and life sciences sectors. Source

Platform Clinical Trials

Dec 9, 2025

Comprehensive Medical LLM Benchmarking Framework Published

CMC journal published comprehensive evaluation of leading Medical-LLMs including GPT-4Med, Med-PaLM, MEDITRON, PubMedGPT, and MedAlpaca across diverse medical datasets. Study introduces domain-specific categorization system aligning models with optimal applications in clinical decision-making, documentation, drug discovery, research, patient interaction, and public health. Addresses deployment challenges including trustworthiness and explainability requirements. Source

Drug Discovery Platform

Dec 9, 2025

AI-Designed Antibodies Approach Clinical Trials

Nature reports that AI-designed antibodies are on the cusp of clinical trials, just one year after first proof-of-concept. Multiple teams using proprietary and open-source tools created antibodies with therapeutic properties comparable to commercial drugs. Chang Liu (UC Irvine) called these advances "remarkably powerful" and capable of "democratizing antibody engineering." This represents acceleration from concept to clinical-ready molecules in under 12 months. Source

Drug Discovery Platform

Dec 9, 2025

AI-Generated Antimicrobials Target Bacterial Membrane Microdomains

Scientific Reports published research using generative neural networks to design antimicrobial compounds targeting specific bacterial membrane microdomains, particularly cardiolipin-rich domains. The study modeled bacterial membranes with distinct lipid distributions and assessed AI-generated candidates via free-energy calculations, demonstrating selective targeting potential for next-generation antimicrobials. Source

Clinical Trials Platform

Dec 8, 2025

NetraAI Platform Demonstrates Precision Clinical Trial Enrichment

Nature npj Digital Medicine introduced NetraAI, an explainable AI platform integrating dynamical-systems modeling, evolutionary feature selection, and LLM-generated insights. Applied to a Phase II ketamine trial for treatment-resistant depression (n=63), it identified high-effect-size patient subpopulations and substantially enhanced treatment effect detection compared to traditional ML approaches, offering a pathway for prospective trial enrichment. Source

Drug Discovery Platform Regulatory

Dec 5, 2025

Frontiers Review: Generative AI in Antimicrobial Resistance

Frontiers in Microbiology published comprehensive review examining LLMs and protein language models for anticipating antimicrobial resistance pathways, designing novel agents, and guiding interventions informed by evolutionary dynamics. The review emphasizes robustness, explainability, and equitable predictions while addressing biosafety concerns around dual-use risks of generative AI in designing resistant strains. Source

Platform Drug Discovery

Dec 4, 2025

AI-Powered Biofoundries Accelerate Protein Engineering

Current Opinion in Biotechnology published review on AI-driven biofoundries accelerating the design-build-test-learn cycle in synthetic biology. Language models, generative AI, and active learning drive protein engineering, with emerging foundational biological models enabling multiscale design from DNA to cells. Cloud biofoundries and multi-AI agents advance toward self-driving laboratories. Source

Clinical Trials Platform

Dec 1, 2025

GPT-4 Achieves 97.9% Accuracy in Clinical Trial Patient Screening

New England Journal of Medicine published results showing GPT-4-based RECTIFIER system achieved 97.9% accuracy screening heart failure patients for clinical trials versus 91.7% for human specialists. The retrieval-augmented generation system processed average 12 clinical notes per patient and could significantly accelerate enrollment timelines while reducing operational costs. Source

Clinical Trials Regulatory

Nov 30, 2025

AI-Literacy Training Enhances Physician-LLM Diagnostic Collaboration

Randomized controlled trial in Pakistan (60 physicians, January-May 2025) showed AI-literacy training enabled physicians using LLMs to achieve 71.4% diagnostic reasoning scores versus 42.6% with conventional resources (27.5 percentage point difference, P<0.001). Study demonstrated physician-AI complementarities, with trained physicians surpassing LLM-alone performance in 38% of cases, highlighting importance of structured training before clinical deployment. Source

Platform Clinical Trials

Nov 30, 2025

Nature Review: LLMs Transforming Biomedicine and Healthcare

A comprehensive review in Nature npj Precision Oncology examined the current state of LLMs across biomedicine and healthcare. The paper explores practical applications including genomics (Evo, gLM, Caduceus), transcriptomics (scGPT), protein structure prediction (ESM-2, ESM3), and clinical decision-making, while addressing ethical concerns and technical challenges for real-world implementation. Source

Clinical Trials Platform

Nov 23, 2025

Bilingual Clinical Drafting AI Agent Deployed with EHR Integration

Publication describing on-premises LLM integration with electronic health records for clinical documentation. The system addresses the rare real-world implementation of LLMs in clinical workflows, providing bilingual capabilities for medical documentation generation while maintaining data privacy through local deployment architecture. Source

Platform Drug Discovery

Nov 26, 2025

Autonomous AI Agent "kai" Conducts Single-Cell Biology Analyses

A bioRxiv preprint introduced "kai," an agentic AI system that uses LLMs to plan and execute single-cell omics analyses through iterative Jupyter notebook interactions. The system combines retrieval-augmented generation across 7,000+ APIs and 6,000 notebooks, demonstrating improved robustness against code errors compared to one-shot generation. kai can autonomously formulate and address research questions on single-cell datasets without human intervention, representing a shift from AI assistance to autonomous scientific discovery. Source

Platform Drug Discovery

Nov 25, 2025

MIT Releases Open-Source BoltzGen for Generative Drug Discovery

MIT researchers released BoltzGen, a fully open-source generative AI model that designs novel protein binders for drug discovery targets from scratch. Building on Boltz-2, it is the first model to unify protein structure prediction with de novo drug candidate generation while incorporating physical and chemical constraints. The model was validated across 26 therapeutic targets in 8 wet labs spanning academia and industry, including tests against traditionally "undruggable" disease targets. Source

Drug Discovery Platform

Nov 24, 2025

UNC Achieves 200-Fold Potency Improvement Using AI-Guided Drug Design

UNC Eshelman School of Pharmacy's Center for Integrative Chemical Biology and Drug Discovery reported that AI-guided generative methods discovered compounds targeting a critical tuberculosis protein in six months, achieving 200-fold enzyme potency improvement in just a few iterations. The lab also released DELi, the first open-source software rivaling commercial tools for analyzing DNA-encoded library data. Source

Clinical Trials Platform

Nov 22, 2025

Multimodal LLM Achieves 93% Accuracy in Clinical Trial Patient Matching

Researchers published validation results for a multimodal LLM pipeline for clinical trial patient matching in Nature Communications Medicine. The system uses visual LLM capabilities to interpret medical records including scans, tables, and handwritten notes without lossy conversions. Results: 93% criterion-level accuracy on the n2c2 benchmark, 87% accuracy across 485 real-world patients from 30 sites matched against 36 trials, and 80% reduction in screening time versus manual chart review. Source

Partnership Funding Drug Discovery

Nov 19, 2025

AI Proteins Secures $41.5M for Generative Miniprotein Therapeutics

Boston-based AI Proteins closed a $41.5 million Series A led by Mission BioCapital and Santé Ventures to advance de novo miniprotein therapeutics designed using generative AI. The company has generated molecules against 150+ targets with multiple programs showing in vivo proof-of-concept, following a $400M research collaboration with Bristol Myers Squibb announced in December 2024. Source

Regulatory

Nov 19, 2025

WHO Europe Issues First Regional AI Healthcare Assessment

The World Health Organization's European office published its first comprehensive assessment of AI adoption and regulation across 50 of 53 member countries. Key findings: 86% of countries cite legal uncertainty as their top barrier to healthcare AI adoption, 78% cite affordability concerns, and fewer than 10% have established liability standards for AI in health settings. WHO Regional Director Hans Kluge warned that without clear data privacy protections and AI literacy investments, the technology risks deepening health inequities. Source

Regulatory Clinical Trials

Nov 18, 2025

Nature Perspective Reveals FDA's Internal LLM "Elsa"

A perspective article in npj Digital Medicine examined AI modernization of clinical trials and disclosed that the FDA has deployed an internal LLM called "Elsa" (powered by Anthropic's Claude) to help staff accelerate clinical protocol reviews and shorten scientific evaluation times. The paper detailed how LLMs enable AI-driven eligibility optimization, reinforcement learning for real-time protocol adaptation, and digital twin modeling for clinical trials. Source