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AI in drug discovery

Structure prediction, generative design and virtual cells: verifiable progress for AI in discovery and the clinic.

141 selectedRelated topicsMethods and resourcesSmall molecules and degradersOriginal research

Latest picks

41–60 of 141
Oct 11Sun
  1. bioRxiv Genomics62

    Spatiotemporal AI modeling of gene regulation and variant effects in the developing human brain

    Seal, an interpretable AI transfer learning framework, models gene expression and variant effects with spatiotemporal resolution across 26 brain regions, 30 cell types, and seven developmental stages of the human brain. Applied to GWAS, it identifies cell types and developmental windows relevant to six neuropsychiatric conditions, and in Simons Simplex Collection autism whole genome data it uncovers a significant burden of de novo regulatory variants in transient fetal excitatory neurons.

    Why it matters: It links disease-associated variants to specific cell types and developmental windows in the developing brain.

  2. bioRxiv Genomics70

    A unified framework for quantitative splicing and transcript prediction

    SpliceAI2 is a deep learning model trained end-to-end on splice site, splice junction, and transcript usage data across multiple mammalian species, and it reconstructs complete transcripts directly from genomic sequence. It improves detection and quantification of splice-altering variants across benchmarks covering cryptic splicing, splicing QTLs, and massively parallel reporter assays. In a rare disease cohort, predicted splice-altering variants account for 15% of the excess genetic burden, with about half in deep intronic regions likely missed by exome sequencing.

    Why it matters: SpliceAI2 quantifies splice-altering variant effects genome-wide, including deep intronic variants exome sequencing misses.

  3. mp.weixin.qq.com69

    Envisagenics and Boehringer Ingelheim Strike Over $1 Billion RNA Splicing Collaboration for Solid Tumours

    Envisagenics and Boehringer Ingelheim entered a multi-year research collaboration and option agreement announced on September 22, 2026, worth more than $1 billion in total. The partners will validate selected tumour-specific targets and develop first-in-class precision therapies for solid tumours, using Envisagenics' AI-driven approach that turns alternative RNA splicing into a drug discovery frontier.

    Why it matters: Large biotech-AI deal targeting RNA splicing for solid tumour therapies.

Oct 10Sat
  1. arxiv.org40

    Learning Probabilistic Logic Programs with Functional Gradient Guided Language Models

    grasp (Gradient-boosted Synthesis of Probabilistic logic programs) learns probabilistic logic programs by treating relational structure learning as functional gradient boosting, with first-order rules as weak learners and an LLM serving as the proposal oracle for the inner search. Across four relational benchmarks, including molecular toxicity prediction (Tox21), mutagenesis, and citation matching (Cora), grasp improves over purely symbolic, neural, and LLM-based baselines while yielding interpretable weighted rule ensembles and retaining boosting guarantees.

    Why it matters: It shows LLM-guided boosting can synthesize interpretable logic programs that beat symbolic, neural, and LLM baselines on molecular benchmarks.

  2. Lancet72

    Conversational diagnostic artificial intelligence in ambulatory primary care: a prospective feasibility study.

    A patient-facing conversational AI, AMIE, was used by 114 patients up to 5 days before urgent primary care appointments, with 98 completing both the AI interaction and the physician visit. Zero prespecified safety stops occurred, and clinical evaluators rated conversations favourably in 87-100% of cases across 17 criteria. PCPs who reviewed the transcript before the visit found it helpful in 33 of 44 cases and said it might have changed their behaviour in 25 of 44.

    Why it matters: First real-world feasibility data on a patient-facing conversational AI in urgent primary care, with zero safety stops.

  3. reddit.com59

    Eli Lilly invests in women's health, backing Absci's ABS-201 drug designed to treat endometriosis by modifying its underlying pathogenesis.

    Eli Lilly is backing Absci's ABS-201, a generative AI-designed anti-prolactin receptor antibody for endometriosis, through a $100 million financing that includes a $40 million strategic investment from Lilly. ABS-201 is designed to block prolactin receptors to modify the underlying pathogenesis of endometriosis, and Phase II trials are expected to start in Q4 2026. The support fits Lilly's stated shift toward women's health, funded by GLP-1 profits.

    Why it matters: Big pharma money is moving into endometriosis, an underserved indication, with a named candidate and Phase II date.

Oct 9Fri
  1. BioSpace54

    TRexBio, Retension raise $161.7M in IPOs; City, Iambic set proposed offering terms

    TRexBio raised $116.7 million in its Nasdaq IPO, selling 8.3 million shares at $14 to fund TRB-061, a fusion protein in early-stage development for atopic dermatitis with a Phase 1b topline readout expected mid-2027. Retension Pharmaceuticals raised $45 million at $12 per share to advance RTN-001, a PDE-5 blocker for hypertension, through Phase 2b and into Phase 3. City Therapeutics expects about $158.3 million and Iambic Therapeutics about $135 million from upcoming offerings.

    Why it matters: Four biotech IPOs in one week signal a reopening of the public markets for drug developers.

Oct 8Thu
  1. BioSpace66

    Big Pharma–backed Matchpoint scores $150M series B, advancing autoimmune asset

    Preclinical Matchpoint Therapeutics has raised a $150 million series B co-led by Nextech Invest and Norwest, with participation from Sanofi Ventures, Atlas Venture, Access Biotechnology and the T1D Fund. The money will advance MPT-062, an oral T cell modulator for autoimmune and inflammatory conditions, into human trials starting next year. Matchpoint also has a July 2025 deal with Novartis, which paid $60 million upfront for its ACE platform and could owe up to $1 billion in milestones plus royalties.

    Why it matters: A large series B funds a first-in-class oral autoimmune program before it reaches human trials, against a market trend favouring later-stage assets.

  2. mittrchina.com72

    Biohub Expands Virtual Biology Initiative With DOE, NIH, Google and Meta to Build AI Biology Data Infrastructure With About $1.8 Billion

    Biohub is joining with the U.S. Department of Energy, NIH, Google DeepMind, Isomorphic Labs and Meta to commit roughly $1.8 billion in funding, data and computing power to the Virtual Biology Initiative, building open data training infrastructure for next-generation AI virtual cell models.

    Why it matters: Tech companies and federal agencies are jointly committing about $1.8 billion to AI biology data infrastructure, showing AI drug development extending from model training to experimental data production.

  3. mp.weixin.qq.com67

    AI Agents Build scBaseCount, the Largest Standardized Single-Cell Database with Over 502 Million Cells

    scBaseCount, published in Cell by researchers from the Arc Institute, the University of California, Stanford University and others, is the largest public single-cell RNA sequencing database built to date. AI agents mined all public 10x Genomics datasets in the Sequence Read Archive, automatically annotated metadata and reprocessed counts under a uniform standard, covering 27 species, 75 tissues and more than 502 million cells. The resource supplies high-quality, large-scale data for training AI models of cell state and for cross-study integration.

    Why it matters: It turns the unstructured Sequence Read Archive into a standardized resource large enough for AI cell-state models.

Oct 7Wed
  1. Nat Biotechnol63

    Decoding systemic vascular health and hypertensive disorders in pregnancy through retinal imaging and Visionary AI.

    Visionary AI, a platform combining retinal imaging with graph-based vascular modeling, predicted hypertensive disorders of pregnancy before clinical onset. In a prospective development cohort of 1,267 pregnancies it reached AUC 0.91 and average precision 0.81 for preeclampsia, and a stability-optimized model transferred without retraining to external validation at AUC 0.81 and AP 0.68, outperforming clinical and deep learning benchmarks. The finding positions the maternal retina as a minimally invasive biosensor for first-trimester risk stratification.

    Why it matters: First-trimester preeclampsia prediction from retinal images outperformed clinical and deep learning benchmarks in external validation.

Oct 6Tue
  1. BioSpace46

    Compliance concerns hinder rollout of AI in drug manufacturing: Survey

    Fewer than 10% of drugmakers are scaling AI in clinical or commercial manufacturing, according to a CRB survey of more than 400 R&D and manufacturing professionals for its 2026 Horizons report. Use is concentrated in nonregulated areas such as administrative workflows (56%) and drug discovery (45%), with accuracy and quality concerns and insufficient validation standards cited as the main barriers. CRB found that 31% of respondents plan to use AI in regulated manufacturing within three years, and another 31% are considering it without a formal plan.

    Why it matters: Shows regulatory and validation concerns are blocking AI adoption in drug manufacturing, with an FDA warning letter already issued.

  2. Nature Biotechnology65

    Decoding systemic vascular health and hypertensive disorders in pregnancy through retinal imaging and Visionary AI

    Hypertensive disorders of pregnancy affect 2–15% of pregnancies and are usually diagnosed after 20 weeks of gestation, once hypertension, proteinuria or organ dysfunction appear, even though low-dose aspirin started before 16 weeks can reduce risk of preterm delivery with preeclampsia by up to 62%. The paper develops retinal imaging with AI to detect the systemic and placental vascular changes that precede symptoms, building on evidence that women with preeclampsia show retinal arteriolar narrowing, increased tortuosity and reduced capillary density before diagnosis.

    Why it matters: It tests a noninvasive retinal imaging route to early preeclampsia risk identification, which current late diagnostics fail to provide.

  3. Nature Medicine60

    MAGIC: an international network for evaluating generative artificial intelligence in global health

    MAGIC is an international network, described in Nature Medicine, for evaluating generative artificial intelligence in global health. Its contributors are based at institutions including Duke-NUS Medical School, Weill Cornell Medicine, the University of Tokyo, the WHO Regional Office for South-East Asia, the International Telecommunication Union, Harvard Medical School and the University of Ghana Medical School. The effort spans biomedical data science, computational biology, clinical medicine and health policy across Asia, Africa, Europe and North America.

    Why it matters: A new international network sets out how generative AI will be evaluated for use in global health.

  4. STAT News49

    STAT+: Utah plows ahead with more health AI pilots for prescriptions, women’s health

    Utah regulators announced a plan to let Nolla Health, a startup backed by $4.5 million from investors including General Catalyst, launch an AI service that assesses patients for mild-to-moderate acne treatment through structured intake and skin photo analysis. The AI may determine whether a low-risk drug from a list is appropriate; a licensed clinician will initially review and sign off on prescriptions, though Nolla may eventually prescribe without human review. The pilot expands Utah's AI sandbox program, in which the Office of Artificial Intelligence Policy can waive regulations for supervised testing of AI products.

    Why it matters: A state regulator is letting an AI product write prescriptions without prior human review, a first for US drug prescribing.

  5. youtube.com69

    ESM Team Describes Training ESMC and Opening Protein Language Models with Sparse Autoencoders

    A Biohub webinar by the ESM team explains the motivation for protein language models and the training of ESMC, which masks single amino acids and predicts them from context with stacked transformer layers. The talk highlights sparse autoencoders from NLP as a way to extract monosemantic features from dense embeddings, with 16000 features chosen to balance specificity against cross-family generalization, enabling explicit clustering of protein space distinct from standard sequence or structure clustering.

    Why it matters: Explains how interpretability tools from NLP are being applied to protein foundation models.

  6. youtube.com77

    ESMFold2 Matches MSA-Based Structure Prediction from Single Sequences

    ESMFold2 predicts protein structures end to end from ESM-C 6B language-model embeddings alone, replacing MSA input with pair folding layers, PARSE recurrence in place of some recycle steps, and chain-independent encoding. Without MSAs it matches older MSA-based models on FoldBench (278 protein-protein interfaces) and beats most open and closed models on antibody-antigen docking (172 interfaces) from a single sequence, improving further with an MSA. About 8 to 16 recurrence loops saturate protein-protein prediction, while antibody-antigen cases keep improving; diffusion needs 20 to 30 steps.

    Why it matters: Single-sequence structure prediction matching MSA models changes how scientists can model proteins and complexes without homologs.

Oct 5Mon
  1. youtube.com64

    CSHL interview: Elham Azizi on AI models for mapping the tumor microenvironment

    Cold Spring Harbor Laboratory interviewed Columbia's Elham Azizi about using machine learning, from probabilistic models to generative and foundation models, to characterize the tumor microenvironment from spatial transcriptomics, including deconvolution of mixed signals in sequencing-based platforms like Starfish. In ER-positive breast cancer, her cell-interaction algorithms suggest cytotoxic T cell infiltration correlates with worse outcomes, possibly because T cells increase tumor dependence on estrogen receptor programs, pointing to better treatment combinations. Her long-term goal is a TME foundation or world model that predicts patient trajectories across treatment doses and schedules, requiring large paired pre- and post-treatment cohorts and out-of-distribution generalization.

    Why it matters: Shows how spatial transcriptomics and foundation models could turn TME analysis into predictive treatment selection.

  2. youtube.com52

    Cross-Species Optic Nerve Single-Cell Atlas With AI Annotation Maps Glia Subtypes in Glaucoma

    A cross-species single-nucleus RNA-seq atlas of the retina and optic nerve covers about 150k nuclei across 5 species, integrating data into a shared space to identify conserved and species-specific cell types. AI-assisted annotation reached 99%, 95% and 90% accuracy at different confidence tiers, speeding manual curation. The work identifies AS1 and OPC3 glial subpopulations changed in the optic nerve and glaucoma context, pointing to glial roles in optic nerve damage.

    Why it matters: A 150k-nucleus, 5-species optic nerve atlas links glial subtypes to glaucoma and speeds annotation with AI.

  3. youtube.com72

    Jennifer Doudna says AI accelerates nuclease design but cannot replace chance discovery

    Jennifer Doudna discussed the Viper system, a new class of programmable nucleases discovered and named by her lab from studies of microbial defense systems, which can be engineered for gene editing and differs structurally from Cas proteins. She described its non-contiguous guide encoding, in which functional elements are not adjacent in sequence, making it harder for AI to learn without biological priors. She said generative and structural models can predict activity and narrow mutation search spaces, but chance observations still drive many breakthroughs.

    Why it matters: A CRISPR pioneer defines what AI can and cannot do in nuclease design for gene editing.

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