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Drug Discovery

44 items across 29 editions. First seen Thu 17 Sep, last seen Sun 1 Nov. Traced across 1 weekly review.

How this story has evolved

From the week in review: the connections, developments and open questions filed under Drug Discovery, newest week first.

Week of 14–20 September 2026

Development · Wed 16 Sep, Thu 17 Sep, Fri 18 Sep
Anthropic opens a life-sciences tier that "removes all safeguards that block life sciences requests" and reports a 4x speed-up of biomolecular models

Anthropic announced the Life Sciences Verification Program on 17 September, giving vetted organisations access to its Mythos, Opus and Sonnet models with safeguards "more permissive for biology-related work" covering tasks "currently blocked in our generally available Fable models". Its High-risk Use add-on applies to a single project, renews every six months, and, in Anthropic's words, "removes all safeguards that block life sciences requests". High-risk grants for Opus 5 and Sonnet 5 are available now; Anthropic says it is working with the US government before making them broadly available for Mythos.

Sunday, 1 November 2026

Monash and NVIDIA post PSICHIC+, a bioactivity model trained on about 82 million protein-ligand records beneficialPreprint

bioRxiv
  • The preprint, posted to bioRxiv on 31 October 2026 by Huan Yee Koh and colleagues at Monash University, Griffith University and NVIDIA, describes PSICHIC+ as "a bioactivity foundation model trained on approximately 82 million protein-ligand records using a distributional objective that preserves quantitative resolution while incorporating variation in data reliability".
  • The abstract says the model predicts a probability distribution over binding affinities from a protein sequence and a ligand SMILES string "in milliseconds", and that in zero-shot virtual-screening benchmarks without target-specific training data "it improves enrichment over regression- and classification-based approaches and performs competitively with leading structure-based methods".
  • The authors report that combining PSICHIC+ with Boltz-2 "improves hit-to-lead affinity prediction over Boltz-2 alone, with mean correlation approaching that of FEP+ on the evaluated benchmarks, without requiring high-resolution protein structures", and that distribution-guided acquisition improved recovery of high-activity compounds in lab-in-the-loop campaigns under fixed screening budgets.
  • The abstract gives no enrichment factors, correlation coefficients or hit rates, so the size of each improvement cannot be stated from it. The work is not peer reviewed. Two authors are NVIDIA employees, which the paper declares.
Drug DiscoveryAI For ScienceNVIDIA

Princeton and Edison Scientific system elucidated four novel natural product structures from 1D NMR spectra beneficialPreprint

bioRxiv
  • The preprint, posted to bioRxiv on 31 October 2026 by Ziyu Xiong and colleagues at Princeton University, with co-authors employed by Edison Scientific, presents ChefNMR2, "an atomic flow-matching model that predicts small-molecule structures from molecular formulae and one-dimensional 1H and 13C NMR spectra", and ChefNMR2-Agent, which "augments our generative model with large language models (LLMs) to reason over and refine candidate structures".
  • The authors report that "across diverse benchmarks, ChefNMR2 substantially outperforms existing tools, offering calibrated confidence estimates and two to three orders of magnitude faster inference than frontier LLMs".
  • They used ChefNMR2-Agent "to elucidate the structures of four novel natural products, matching human-chemist accuracy in a fraction of the time".
  • The abstract names no benchmark scores or baselines, so the size of the reported improvement over existing tools cannot be stated from it. The work is not peer reviewed. Declared funders include the NIH, the Eric and Wendy Schmidt Transformative Technology Fund and Anthropic's AI for Science Program; three authors declare employment at Edison Scientific.
AI For ScienceDrug DiscoveryAgents

Genome Biology benchmark of eight antibody language models finds pretraining data can be cut to 576,000 sequences

Genome Biology
  • The paper, published on 31 October 2026, benchmarks eight antibody language models — AntiBERTy, AntiBERTa, AntiBERTa2, AbLang, AbLang2, ReprogBert, BERT2DAb and BALM — across five downstream antibody-design tasks: HER2-binding prediction, SARS-CoV-2-binding prediction, binding-affinity regression, paratope prediction and complementarity-determining region prediction.
  • The authors report that "AbLang, AntiBERTa2, and AbLang2 are the best models overall, with distinct task-specific strengths, whereas BALM demonstrates superior performance in binding-affinity regression", and that for binary tasks such as HER2-binding and paratope prediction "performance differences among the models are minimal".
  • On data scale: "reducing the pretraining dataset to 576,000 antibody sequences has a negligible effect on model performance, with some models even showing improvements".
  • The abstract reports no numeric scores for any task, so the margins between models cannot be stated from it. The paper is peer reviewed and open access.
Drug DiscoveryAI For ScienceEvals

Thursday, 29 October 2026

Anthropic expands AI for Science to science funders and institutes including Sanger, Crick and EMBL beneficialCompany claimSingle source

Anthropic
  • Anthropic says the AI for Science program, begun in May 2025, "gives free Claude credits to researchers working on high-impact scientific projects" and has "supported hundreds of projects". The expansion adds science funders and societies: the Alfred P. Sloan Foundation, the Simons Foundation, Schmidt Sciences, the Open Source for Science Fund at Renaissance Philanthropy, the US National Science Foundation (which Anthropic describes as a "$9 billion federal agency") and CAS, a division of the American Chemical Society.
  • Named research institutions include the Space Telescope Science Institute, the Wellcome Sanger Institute, the Francis Crick Institute ("more than 100 research groups"), Imperial College London's Department of Life Sciences, EMBL and Gladstone Institutes. gnomAD, one named project, pools DNA data from "over 800,000 people".
  • Routing frontier-model access through funders rather than individual grants is the change here: the institutions named run shared infrastructure for large parts of public biology and astronomy.
  • Anthropic gives no credit amounts, no total value and no pricing for the "discounted Team plan for scientists" it mentions. Its introduction refers to "20 scientific foundations, institutions, and research groups" while the lists name 18, and the post reports no scientific results.
AnthropicAI For ScienceDrug Discovery

Wednesday, 28 October 2026

Anthropic reports lab results from six drug-development partners and acquires one-person biotech Mithril Medicine beneficialCompany claim

Anthropic
  • Anthropic names six partner organisations: BigHat Biosciences, Bristol Myers Squibb, Divergence Labs, Enveda, Generate Biomedicines and The Column Group. At Enveda, tested on 1,061 pages of documentation, Anthropic says Claude "caught all 11 issues that human reviewers had spotted—and flagged seven they'd missed", and "reduced human review time by 89%, from over 25 hours to less than three", with a first draft of one regulatory document taking four hours against about three months by hand. At Divergence Labs it says Claude generated "roughly 35,000 antibody designs in about a month", yielding three designs that bind IgE and 20 that can dislodge it.
  • At Generate Biomedicines, six scientists used Claude Code to propose CAR T designs; Anthropic says one was "about 70% shorter than the standard version" and in lab tests "allowed immune cells to make roughly 10 times as many receptors". At BigHat, about 1,010 antibody-fragment variants were made and tested over about 10 weeks, with the best binding "more than 10x more tightly than the originals". Anthropic also says it acquired Mithril Medicine, a one-person company whose founder used Claude to coordinate work with 11 external labs, taking a muscular dystrophy candidate from idea to promising mouse results in under 18 months.
  • Every figure is Anthropic's own and none is independently verified. Anthropic says the work "is mostly preclinical" and most candidates are not yet in human testing; it puts possible first human trials for the Mithril candidate "as soon as late 2028, if results hold up". It flags its own hedges — Divergence's hits only "appeared" to outperform the standard medication, and BigHat found its heat-stability and clumping metrics misleading and changed them mid-project.
AnthropicDrug DiscoveryAI For ScienceHealthcare

Monday, 26 October 2026

AlphaGenome-fed classifier scores germline variant pathogenicity at AUC-ROC 0.9753 on 12,385 test variants beneficialPreprintSingle source

bioRxiv
  • AGVarPred is a gradient-boosted classifier predicting the pathogenicity of germline mutations across single-nucleotide variants and indels, combining gnomAD population allele frequency, Ensembl VEP functional consequence annotations and "cell-type-specific functional genomic scores from the AlphaGenome platform". On a gene-level split of 134,002 high-confidence ClinVar variants across 9,222 genes, the preprint reports AUC-ROC 0.9753 (95% CI 0.9731–0.9775) and AUC-PR 0.9606 on 12,385 scored internal test variants.
  • The reported gain comes from the genomic model. A paired DeLong analysis puts the full model ahead of an allele-frequency-plus-VEP model by ΔAUC = 0.1539 (95% CI 0.1489–0.1589; p < 1 × 10⁻³⁰⁰).
  • The stated gap the authors are addressing is that existing in-silico predictors are "restricted to missense variants from the coding region", leaving splice-site, UTR and intronic variants under-scored. Authors are at ICAR–Indian Institute of Agricultural Biotechnology in Ranchi, BRIC–Centre for DNA Fingerprinting and Diagnostics in Hyderabad and the University of Waterloo. This is a preprint posted 25 October, not peer reviewed, and the test set is internal — no external clinical validation is reported.
HealthcareAI For ScienceDrug Discovery

Sunday, 25 October 2026

Dutch preprint: deep learning on routine slides predicts HPV status in vulvar cancer at AUC 0.84, but sensitivity is 0.68 beneficialPreprintSingle source

medRxiv
  • The preprint, posted 24 October, reports two attention-based multiple-instance learning models built on features from H-optimus-1: HERA, predicting HPV/p16 status in vulvar squamous cell carcinoma, and HERMES, predicting its three molecular subtypes. Models were developed on 644 patients from three Dutch cohorts and tested on 286 patients from two independent cohorts. In independent testing HERA reached "an area under the curve (AUC) of 0.84 (95% CI 0.75-0.91), with specificity 0.93, negative predictive value 0.94, and sensitivity 0.68", and HERA-predicted groups reproduced the prognostic separation of HPV testing (log-rank p<0.001).
  • HERMES reached "a macro-average AUC of 0.70 (0.66-0.75)", with 0.80 for HPV-associated tumours but only 0.68 for HPV-independent/p53-abnormal and 0.63 for HPV-independent/p53-wildtype. Its predicted classes reproduced the prognostic hierarchy, with 5-year recurrence rates of 14.0%, 31.1% and 43.3% respectively (p=0.0051).
  • The stated use is retrospective: the authors say HERA "could assign HPV/p16 status to large retrospective VSCC cohorts lacking ancillary testing, without consuming tissue", because the standard p16/p53 immunohistochemistry consumes tissue and is often incomplete in older cohorts.
  • The authors are explicit about what is missing. Before clinical use, "external validation of its discrimination and calibration across scanners and populations is required", and HERMES "requires improved distinction between the HPV-independent subtypes". A sensitivity of 0.68 means the model misses close to a third of HPV-positive cases. The work is a preprint, not peer reviewed; authors are at Leiden University Medical Center, University Medical Center Groningen and the AIRMEC Consortium.
HealthcareAI For ScienceDrug Discovery

Saturday, 24 October 2026

Pathos AI raises $237m led by Bristol Myers Squibb and licenses Tempus neuro data for $55m Company claim

Reuters·STAT News
  • Reuters reports: "Pathos AI also raised $237 million in a Series E funding round led by Bristol Myers that valued the New York-based company at about $3 billion, with participation from existing investors." STAT gives the same $237 million and "approximately $3 billion". The collaboration with Bristol Myers Squibb and Tempus AI targets Alzheimer's disease and other neurological disorders.
  • Reuters says "Pathos will license the data from Tempus under a five-year, $55 million agreement", with Tempus building "a dataset from hundreds of thousands of patients with neurological disorders". Bristol Myers gains non-exclusive strategic access to Pathos's Foundry platform across its broader portfolio plus three-year exclusive rights to use it for neuroscience development programmes.
  • STAT describes Foundry as a proprietary model that "operates thousands of agents" calculating the probability of success for Pathos's clinical assets from clinical data, genomics, biological specimen data and competitors' activity, used to decide which drugs to pursue, in which patient populations, and how to design trials.
  • The Foundry description is the company's own and no performance figures are given for it. Proceeds are to advance the experimental cancer drug JSKN016 and an AstraZeneca-partnered breast cancer drug; no clinical result is attributed to the platform, and most of the STAT article is behind a paywall we did not read past.
Drug DiscoveryFundingHealthcareAgents

Treasury adds biotechnology to proposed outbound investment rules, with comments due 7 December Update

Federal Register·STAT News
  • The Federal Register record for the proposed rule says written comments must be received by 7 December 2026 under docket TREAS-DO-2026-0595, and that it implements Subtitle C of the Comprehensive Outbound Investment National Security Act, codified at Title VIII of the Defense Production Act, requiring notification for "notifiable technologies" and prohibiting transactions involving "prohibited technologies".
  • STAT reports that Treasury "added biotechnology to the list of industries subject to COINS restrictions, joining the likes of semiconductors, artificial intelligence, and quantum technology", and that the proposal "would severely restrict future U.S. investment in the Chinese biotech sector but still allow American drugmakers to buy the rights to medicines invented in the country".
  • STAT says the proposal would prohibit US companies from entering financial transactions with Chinese biotech companies or Chinese manufacturers of biotechnology-related equipment and tools, citing the research firm Veda.
  • This updates the Treasury proposal reported here on 23 October; new are the biotechnology sector addition, the licensing carve-out, the comment deadline and the docket number. The rule is a proposal, not final, and the Federal Register lists a publication date of 26 October — after this edition's window.
China distillation and export controlsExport ControlsUS Federal PolicyDrug DiscoveryChina

Friday, 23 October 2026

Radical Numerics says AI-designed bacteriophages were given to a patient under compassionate use, with no results published mixedCompany claimSingle source

The Register
  • The Register reported on 22 October that San Francisco AI lab Radical Numerics announced a cocktail containing bacteriophages designed by its Omnii-1 model was administered to a human patient under a compassionate-use arrangement, which the company claims is a first. The company said: "The cocktail containing Omnii-1-designed phage variants eliminated the patient's antibiotic-resistant isolate in vitro… The investigational cocktail was subsequently administered to the patient under compassionate use."
  • The in vitro result is laboratory testing against bacteria isolated from the patient's infection. The company published nothing about what happened in the human patient, citing the need to protect privacy, and did not respond to The Register's questions.
  • The company's own caveats, as reported: it does not want to suggest the therapy is generally safe or effective, and observations from a single patient "don't establish much of anything". The Register notes the treatment may not fully eliminate the infection.
  • The patient's infection is not named. Pseudomonas aeruginosa appears in the write-up only as a target for testing Omnii-1's phage-host prediction, where The Register says the model "performed competitively" against a specialised model, and nothing suggests the patient had it. The work builds on Stanford and Arc Institute AI-designed E. coli phages reported last year.
Drug DiscoveryAI For ScienceHealthcareBio Risk

Thursday, 22 October 2026

Neural-ODE model nominated three rescue targets for mutant B cells and mouse experiments confirmed all three beneficial

Nature Biotechnology
  • The paper describes DynaVelo as "a generative neural ordinary differential equation model that learns the joint dynamics of gene expression and transcription factor (TF) motif activity from single-cell multiome data," built so "the model can be perturbed in silico and not only used to draw trajectories." It "recovers the bifurcating in vivo dynamics of wild-type murine germinal center (GC) B cells and reveals how these dynamics change under heterozygous loss of epigenetic regulators Arid1a and Ctcf."
  • The prediction was then tested in animals: "Perturbing Arid1aHet GCs in silico identified Ctcf, Stat3 and Bcl6 as rescue targets for Arid1a loss. In vivo experiments in double-heterozygous mice confirmed rescue of the Arid1aHet dark-zone phenotype in all three cases, and multiome profiling of Arid1aHet;CtcfHet GCs validated the predicted shift toward wild-type dynamics."
  • This is the shape of AI-for-science result worth noting: a model that makes a specific, falsifiable nomination which wet-lab work then confirms, rather than a benchmark score. Authors are Karbalayghareh, Pelzer, Chin, Melnick, Barisic and Leslie.
  • The work is in mice and makes no clinical claim. A bioRxiv predecessor was posted in April 2025, so this is the peer-reviewed version of existing work. The full text is paywalled beyond the abstract, so the figures above are the abstract's own.
AI For ScienceDrug Discovery

Wednesday, 21 October 2026

Radical Numerics says AI-designed phages were given to a patient with an antibiotic-resistant infection beneficialCompany claimSingle source

Radical Numerics·Business Wire
  • The company's post, dated October 20, says: "In the world's first clinical use of AI-designed phages, designs generated by Omnii-1 were administered to a patient with an antibiotic-resistant bacterial infection under an authorized, physician-led compassionate-use pathway." It says the cocktail eliminated the patient's antibiotic-resistant isolate in vitro before administration.
  • On the model's design performance, the company reports that in a T7 tail-fiber design round, "[a]t 11–15 amino acid changes, 69.9% of designed variants were recovered, compared with 4.8% of random controls," and that "[o]f 6,694 variants with measurable activity, 2,161 exceeded wild type," some with up to 28 amino acid changes, where random controls exceeding wild type had at most five. On cross-species prediction, adding E. coli interaction data raised average precision on held-out Pseudomonas aeruginosa strains from 0.570 to 0.608, surpassing GenoPHI at 0.581.
  • The Business Wire release says the phage was designed "within weeks" and administered at a clinical site under physician compassionate-use access, and quotes Dr. Marisa Azad, an infectious diseases physician-scientist at The Ottawa Hospital. It also says that in biodefence testing the model identified AI-generated pathogen sequences that conventional screening tools missed, and that the company is collaborating with Lawrence Livermore National Laboratory.
  • The company explicitly is not disclosing the patient's clinical course or outcomes, so nothing is known here about whether the treatment worked. All figures are the company's own and not peer reviewed. The company's materials name no regulator, referring only to "the relevant government regulatory authorities" overseeing compassionate use; a Daily Caller report citing the New York Times says company officials said the FDA approved the methodology, which the company's own materials do not state.
Drug DiscoveryAI For ScienceHealthcareBio Risk

OpenAI acquires Orchestra Bio and folds its drug-operations agents into the Rosalind life-sciences suite Company claim

TechCrunch·Becker's Hospital Review
  • TechCrunch reports OpenAI acquired Orchestra Bio, "a four-year-old AI biotech software startup," on Tuesday; terms were not disclosed, and the startup "had reportedly raised around $20 million as of 2025 from backers like Andreessen Horowitz, Atria, and 8VC."
  • Orchestra builds AI agents that handle drug-discovery operations — TechCrunch says "from tracking finances to resolving scheduling issues with clinical trials." Becker's says the agents let scientists hand off monitoring and tracking of studies, identifying affected activities and timelines when, for example, a failed manufacturing batch could delay a trial start.
  • OpenAI is adding the team and technology to Rosalind, its life-sciences model, and the related Rosalind Workbench tool. TechCrunch says OpenAI names Moderna and Novo Nordisk among Rosalind's customers.
  • The price was not disclosed, the funding figure is reported rather than confirmed, and the customer list is OpenAI's own. OpenAI's announcement page could not be opened from here, so both accounts above are the reporting of it.
OpenAIDrug DiscoveryHealthcareAgents

Saturday, 17 October 2026

Google Research agents reach a 42.1% aptamer hit rate at 98.5% specificity after three wet-lab cycles beneficialCompany claimSingle source

Google Research
  • In a post by Pooja Rao, Petar Sirkovic and Annalisa Pawlosky, Google Research reports three design-test-iteration rounds against alpha-thrombin, each cycle taking 7 to 10 days. Round 1 produced 0% binders, with fixed primer overhangs disrupting folding in 48% of sequences; Round 2 reached a 26.0% hit rate after the agents added clamped flanking stems; Round 3 reached 42.1% at 98.5% specificity.
  • Against myoglobin the agents went backwards before recovering: in Round 2, G-quadruplex motifs made up 63% of designs, specificity fell from 75.0% to 19.1% and the bead-corrected hit rate fell from 10.2% to 3.7%, because most of those binders were sticky and bound off-target proteins and bare beads. Round 3 restored specificity to 72.4% and reached a 33.5% on-target hit rate, which Google describes as roughly ninefold over Round 2.
  • For de novo FAP binder design, Google says surgical epitope excision sped up the structure re-folding step by 8.6x and that at equal compute human-agent collaboration nearly doubled the in-silico hit rate of a scientist working alone, with top designs yielding single-digit nanomolar affinity binders. In a self-play campaign reconstructing blinded dye mixtures, its VERID agent improved accuracy more than fivefold across runs.
  • All figures are Google's own and the three underlying papers are hosted as PDFs with no arXiv identifiers. Google does not state the baseline for the fivefold VERID figure, gives no exact affinity value for the FAP binders and gives no percentages for the human-agent comparison.
AI For ScienceDrug DiscoveryAgents

Friday, 16 October 2026

Preprint: protein design models backdoored after 200 poisoned pre-training examples under a ramped schedule harmfulPreprintSingle source

arXiv
  • The authors pre-trained 300-million-parameter ProtGPT2 and ProGen2 variants on poisoned protein sequences and report that "Under ramped exposure, the earliest majority activation occurred after only 200 contaminated examples, while for the uniform schedule earliest activation occurred around 5000 poisoned samples".
  • In a separate retrieval-corpus attack that changed no weights and no decoding settings, the paper reports "A single query-aware record captured a top-16 retrieval position for 58% of templated requests and 97% of free-text requests".
  • Conditional on successful poisoned retrieval, the abstract reports that 62% of requests produced altered sequences.
  • Authors are at the University of Texas at Austin, in the College of Pharmacy and the Department of Computer Science, with Greek research institutes. The work is a preprint on model-level poisoning, not a demonstration of harm from any deployed biodesign service.
Bio RiskAI For ScienceDrug DiscoveryAlignment

SimBioSys says FDA cleared TumorSight Risk, an MRI-derived breast cancer recurrence risk tool, under 510(k) beneficialCompany claim

SimBioSys
  • The company says TumorSight Risk estimates individualised 5- and 10-year recurrence risk from pre-treatment breast MRI plus pathology and clinical data, for adult women with HR-positive, HER2-negative, Stage I-IIIA breast cancer, outputting a 0-100 score with low, intermediate and high categories.
  • Validation was a retrospective, multi-institutional, blinded study of 2,129 patients at Mayo Clinic, Emory University, Stanford Medicine and Mercy Medical Cedar Rapids, presented at the 2026 ASCO Annual Meeting.
  • The release reports high-risk patients had 3.3-fold higher recurrence risk than low-risk patients, each 10-point score increase was associated with a 22% increase in relative recurrence risk, 5-year recurrence ranged from 2.2% (scores 0-10) to 13.6% (scores 58-100), and 10-year recurrence from 6.2% to 34.2%, with the association holding after adjusting for stage, grade, age, race and treatment (p=0.002).
  • The figures come from the company's own release, which notes Mayo Clinic has a financial interest in SimBioSys. The release does not report sensitivity, specificity, or any comparison against existing genomic assays, and the validation was retrospective.
HealthcareFDADrug Discovery

Insilico Medicine preprint reports AUROC 0.828 for predicting clinical trial efficacy from registration data PreprintCompany claimSingle source

medRxiv
  • The preprint describes separate machine-learning models for efficacy and safety or toxicity endpoints across phases I-IV, trained only on features available at trial registration, producing a Predictive Trial Index from 0 to 1 inside an agentic system the authors call inClinico Agent.
  • On a test set of trials initiated in 2021 or later, the authors report efficacy-model AUROC 0.828 (95% CI 0.812-0.845; n = 2,347) and safety or toxicity-model AUROC 0.809 (95% CI 0.787-0.831; n = 1,485).
  • The authors introduce ClinBench, a prospective benchmark of 2,040 ongoing trials, evaluated by inClinico Agent and five frontier large language models.
  • All authors are affiliated with Insilico Medicine, which sells commercial AI software, and they state the curated training database is not publicly available. The work has not been peer reviewed and the prospective benchmark has not yet reported outcomes.
Drug DiscoveryHealthcareAgentsEvals

Wednesday, 14 October 2026

Zealand Pharma pays Generate Biomedicines $30 million upfront in a protein-design deal worth up to nearly $1.2 billion Company claim

GlobeNewswire (via StockTitan)
  • Generate Biomedicines receives $30 million upfront plus up to $35 million in early preclinical milestones, with total eligible milestones of nearly $1.2 billion including development, regulatory and commercial milestones, plus tiered royalties on future worldwide net sales, per the companies' October 13 release.
  • The agreement covers two initial programs in obesity and other metabolic diseases, with an option for Zealand Pharma to add a third, and aims at multispecific proteins. Generate leads research through lead-candidate selection; Zealand Pharma takes clinical development, regulatory submissions and worldwide commercialization.
  • It prices generative protein design against established peptide chemistry in the most commercially contested therapeutic area, obesity.
  • The release describes the Generate Platform as combining generative AI with high-throughput experimental validation but reports no preclinical or clinical data. Nothing here is a trial result; the milestone total is contingent and the upfront payment is the only committed figure. GlobeNewswire's own page could not be opened, so the release text was read via StockTitan's reproduction.
Drug DiscoveryAI For ScienceFundingHealthcare

Sunday, 11 October 2026

Princeton's Seal model predicts brain gene regulation across 26 regions, 30 cell types and seven developmental stages beneficialPreprintSingle source

bioRxiv
  • The preprint, posted October 11 by Y. Hao, C. Y. Park, C. T. Theesfeld and O. G. Troyanskaya, describes Seal as "an interpretable AI transfer learning framework for genome-based modeling of gene expression and variant effects with spatiotemporal resolution across 26 brain regions, 30 cell types, and seven developmental stages".
  • Applied to genome-wide association studies, the authors say Seal identifies cell types and developmental windows relevant to neuropsychiatric disease risk, "revealing shared and distinct regulatory architectures across six neuropsychiatric conditions that align with clinical trajectories". In Simons Simplex Collection autism whole-genome data, they report Seal "uncovers a significant burden of de novo regulatory variants in transient fetal excitatory neurons".
  • The abstract gives no accuracy figures, held-out benchmark or comparison against existing variant-effect predictors, so the claim at this stage is coverage and interpretability rather than measured performance. Not peer reviewed.
AI For ScienceHealthcareDrug Discovery

Saturday, 10 October 2026

Preprint: de novo designed miniprotein blocks hASIC1a and cuts stroke infarct volume by more than 50% in mice beneficialPreprint

bioRxiv
  • The preprint, posted October 9, reports two de novo designed hASIC1a-specific miniproteins produced by "a platform that integrates computational miniprotein design, high-throughput automated patch clamp screening, biophysical and structural characterisation, and in vivo validation". The lead compound, Denasin1, is "a subtype-selective hASIC1a inhibitor … which achieves near-complete inhibition with nanomolar potency".
  • The headline in vivo result as written: "Denasin1 reduces infarct volume by > 50% in a murine model of ischaemic stroke." Acid-sensing ion channels cause neuronal death when excessively activated, such as during ischaemic stroke, and the authors say this "acidosis-driven injury pathway remains unchallenged by existing therapeutics".
  • The authors report that Denasin1 "is disordered in solution but adopts the computationally predicted helix-turn-helix fold when bound to the extracellular acidic pocket of hASIC1a" — the designed structure appearing only on binding.
  • This is a preprint and has not been peer reviewed. The result is in mice; no human data, dosing or safety work is reported, and the authors present the platform as "a route to develop" such modulators rather than a clinical candidate.
Drug DiscoveryAI For ScienceHealthcare

Thursday, 8 October 2026

NIH says it will coordinate with DOE and Biohub to build "SI-ready" data for predictive models of human biology Single source

National Institutes of Health
  • NIH said on 7 October it "is coordinating with the U.S. Department of Energy (DOE), Biohub and other partners to develop the data and resources needed to develop Super Intelligence (SI) models that can better predict how cells and biological systems respond to disease and potential interventions", through its Bio Genesis Mission.
  • NIH says it will bring together existing biomedical datasets, national data infrastructure and research programmes, naming repositories catalogued by the National Library of Medicine and the National Center for Biotechnology Information, plus Common Fund programmes already building coordinated biological atlases and shared data standards.
  • The stated goal, from NIH Deputy Director Nicole Kleinstreuer, is "universal cell models with sufficient biological complexity to predict how any cell responds to an intervention", with "substantially faster timelines for medical breakthroughs" than laboratory experiments alone. Biohub's Alex Rives says a virtual cell "will require coordinated data generation efforts at a national and international scale".
  • This is a coordination announcement with no budget, timeline, milestones or named datasets committed, and the "Super Intelligence" framing is the agency's own wording rather than a technical claim about any existing model. It follows the Justice Department's relabelling of "artificial intelligence" as "super intelligence", reported in yesterday's edition.
HealthcareAI For ScienceUS Federal PolicyDrug Discovery

Saturday, 3 October 2026

Xaira publishes antibody design results, including 7 functional binders from 60,000 designs against a GPCR beneficialCompany claim

Xaira Therapeutics
  • Xaira says its XA-4 programme, aimed at a G-protein coupled receptor it describes as previously seemingly intractable, screened a library of 60,000 designed VHHs and confirmed 7 binders by flow cytometry out of 92 selected candidates. The best candidate showed an IC50 of 183 nM in a β-arrestin antagonism assay and a cell-binding EC50 of 32 nM, with cross-reactivity to cynomolgus and mouse homologs.
  • For XA-1, Xaira says it designed 182 VHH sequences for a 10% binder rate, 18 confirmed binders, with the best at 17 nM against the human target and 83 nM against a cynomolgus surrogate; it reports 3 weeks to a progressable binder and 7 weeks total to a lead molecule.
  • On two GPCR benchmarks Xaira reports a 35% hit rate for CXCR4, 32 of 91 screened designs, with a best EC50 of 12.8 nM against a control at 8.6 nM, and 29% for APJ, 26 of 91, with a best EC50 of 8.4 nM against a control at 8.8 nM. It says a model version it calls X-Design Vega "triples" the rate of human-like VHH sequences meeting all developability criteria.
  • Every number is Xaira's own, in a company blog post rather than a peer-reviewed paper, and the targets for XA-1 and XA-4 are not named. These are in-vitro binding and cell-assay results: no animal efficacy, no toxicology and no clinical data are reported. Fierce Biotech's write-up would not open for either fetcher.
Drug DiscoveryAI For ScienceHealthcare

Friday, 2 October 2026

Anthropic guest post: 36 manuscripts in 18 fields in three months, and 30 Feynman integrals computed end to end beneficialCompany claim

Anthropic
  • In a guest post on Anthropic's research blog, theoretical physicist Matthew Schwartz reports "36 manuscripts in 18 fields with 19 coauthors over three months," drawn from some 400 candidate problems, using a harness he calls BootLoops, released the same day under the MIT License with copyright held by Anthropic PBC.
  • Among the results: 30 integrals "BootLooped from end to end," being "15 reproductions of known results by this new method and 15 that had never before been computed," including elliptic Feynman integrals; an analysis of "5.7 billion pairs of nearby mutations in genomes from the 1000 Genomes Project"; a word-stress database covering 6,072 languages built from a bibliography of 160,000 phonology works; and an economics collaboration covering 4,452 papers in five leading journals.
  • The claim is about throughput across fields rather than a single discovery, and the harness is public, so the method can be tried by others. Schwartz reports Claude working "at 20 times the speed" of manual work on parts of the physics.
  • Schwartz was a visiting researcher at Anthropic during the project, so this is a company-published account of its own model by a funded collaborator, and none of the 36 manuscripts has been peer reviewed through this post. The post also documents failure modes, including the model declaring victory early and giving time estimates far too long or too short.
AnthropicAI For ScienceDrug Discovery

Thursday, 1 October 2026

Google DeepMind publishes SynthIDBio in Nature: watermarked AI-designed proteins bind as well as unwatermarked ones beneficial

Nature·Google DeepMind·Nature news
  • The paper introduces "SynthIDBio, a family of methods for watermarking protein sequences and structures to establish the provenance of those generated with AI". SynthIDBio-sequence applies SynthID-text's tournament sampling inside ProteinMPNN; SynthIDBio-structure is "a fine-tuned AlphaFold3 model".
  • In vitro work used 15 backbones per target against the SARS-CoV-2 receptor binding domain, VEGF-A and PD-L1, with "222 non-watermarked sequences and 267 watermarked sequences for each watermarking setting". The authors report "no significant population-level differences in SPR-derived binding affinities between non-watermarked and watermarked binders", and that at a g-value threshold calibrated for a 0.1% false-positive rate they "automatically obtain a 100% TPR for detecting these designs". For structures, "The TPR exceeds 99.8% for all models" at a 0.1% false-positive rate.
  • The paper states plainly that the watermark can be stripped: "Based on a resequencing attack performed on 38,396 binders, this approach effectively removes the watermark", cutting the estimated hit rate to 97% for SC2RBD, 70% for PD-L1 and 66% for VEGF-A with filters applied, and 33%, 20% and 3% without. Google DeepMind says it is publishing the methods paper, open-sourcing the code and in vitro data, and releasing the weights.
Google DeepMindBio RiskAI For ScienceDrug Discovery

HHS and ARPA-H launch SURPASS, a five-year programme to rebuild clinical trials around AI

ARPA-H·STAT·KUT
  • ARPA-H says SURPASS — "Simulation-augmented, Real-time Platform Adaptive Seamless Trials" — aims to speed up evaluation of drugs and biologics using computational models, real-time analysis and automation, across three technical areas: a phaseless design engine, a continuous inference engine and an agentic operations layer. It cites current clinical drug development as taking "10+ years, costs ~$2 billion, with 90% failure rate".
  • Three companion projects were announced alongside it: STACK, which uses AI to activate clinical sites and expand trial capacity; COMMONS, a nationwide data infrastructure with a privacy-by-design consent architecture; and CINCH, which lets patients contribute real-world data and find relevant trials.
  • STAT reports the five-year programme "will kick off later this fall" by accepting ideas from cross-disciplinary teams, and that "the initial announcement did not note how much funding was designated for the new initiatives" — no dollar figure has been attached to it. KUT reports HHS Secretary Robert F. Kennedy Jr. unveiled the programme in Austin, with UT Austin's Dell Medical School leading STACK and COMMONS.
HealthcareUS Federal PolicyDrug DiscoveryAgents

Wednesday, 30 September 2026

Microsoft Research unveils Quine, a biology research system, with Broad Institute pancreatic-cancer results beneficialCompany claimSingle source

Microsoft Research
  • Microsoft describes Quine as a multimodal AI research system building "a world model of biology" across genomics, proteins, chemistry, cellular state and bioimaging. With researchers at the Broad Institute of MIT and Harvard, it was used to predict and rank compounds by their capacity to shift pancreatic cancer cells between therapeutically relevant states.
  • Microsoft says Quine's top-ranked compounds for classical-to-basal state transitions "produced the largest intended shifts" when tested in wet-lab assays, and that Quine also predicted movement toward a third distinct phenotype which experiments confirmed. It says the process "from rapidly narrowing the compound search space to prioritizing a handful of promising candidates to be validated in the lab—took just one weekend".
  • Access is limited to a Quine Fellows programme and selected research collaborations, with planned expansion through Microsoft Discovery.
  • The post gives no hit rate, no count of compounds screened and no comparison against a non-AI baseline, so the size of the advantage is not stated. There is no peer-reviewed publication attached, and every claim here is Microsoft's.
MicrosoftAI For ScienceDrug DiscoveryHealthcare

Ortet launches as a health AI lab with a $500 million commitment from Thoreau and ex-Genentech founders Company claimSingle source

Business Wire
  • The company announced "a $500 million commitment from Thoreau" to build a full-stack health AI platform spanning compute and data infrastructure, frontier research, and model development and deployment.
  • Co-founder and chief executive Kyunghyun Cho is described as the Glen de Vries Professor of Health Statistics at NYU, a co-developer of the attention mechanism and the Gated Recurrent Unit, who spent five years at Prescient Design and established Genentech's frontier research team after its acquisition. Co-founders include Keunwoo Choi as chief AI officer (previously Upstage, Spotify, ByteDance and Genentech), Henri Dwyer as chief technology officer (who "Built and managed the largest GPU cluster in life sciences at Genentech"), Jeff Hammerbacher as chairman, and Elman Mansimov as chief science officer.
  • Cho is quoted: "Patient health should improve with every interaction, but today its data, knowledge, and infrastructure remain too fragmented for that learning to happen at scale."
  • This is a company press release; no product, model, dataset or clinical result exists yet, and a "commitment" is not the same as capital deployed. Independent coverage the same day by Endpoints News and Axios Pro was paywalled to both of our fetchers, so every figure above comes from the release.
HealthcareDrug DiscoveryFundingAI For Science

Tuesday, 29 September 2026

bioRxiv preprint: 1.6-billion-parameter RNA model designs ribozymes reaching wild-type activity in experiments mixedPreprintSingle source

bioRxiv
  • "RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design", posted 28 September 2026 with corresponding author Joy S. Xiang of the University of California, Riverside, describes RNASeek as "a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation", fine-tuned to predict ribozyme self-cleavage activity and viral mRNA stability, with those predictors used as reward models under Group Relative Policy Optimization to steer generation.
  • The abstract reports: "Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs."
  • Wet-lab validation of generated sequences is the part that distinguishes this from in-silico-only generative biology claims. It is a preprint and has not been peer reviewed; the abstract gives no counts of sequences designed or tested. The bioRxiv page returned HTTP 429 to both our fetchers, so the abstract was read through bioRxiv's own API record for the same DOI.
AI For ScienceDrug DiscoveryBio Risk

Monday, 28 September 2026

MIT: machine-learning-selected excipient mix keeps mRNA-LNP vaccines stable a year at room temperature in mice beneficial

MIT News·Phys.org
  • MIT News reported on 28 September that a formulation from Ana Jaklenec and Robert Langer's group, with lead authors Jinbi Tian and Khanh Tran, kept mRNA lipid nanoparticle vaccines stable "at 37 degrees Celsius (98 degrees Fahrenheit) for two months, or at room temperature for one year". Current RNA vaccines "need to be kept cold (-20 to -80 degrees Celsius)".
  • The team analysed "nearly 50 FDA-approved excipients" and selected "five of the most promising" for machine-learning optimisation. MIT News quotes the rationale: "It's really hard to run thousands of experiments, so this algorithm allows us to more easily achieve formulations with features that we want — in this case, stability", and says the algorithm converged "in just a handful of iterations, rather than the exhaustive search that would normally be required".
  • MIT News reports that "Mice that were vaccinated with these particles, even after long-term storage, showed equivalent immune responses to mice that received vaccines carried by LNPs similar to the original Moderna formulation." Phys.org gives the citation as "Accelerated discovery of thermostable mRNA–lipid nanoparticle vaccines using data-efficient AI", Nature Biotechnology (2026), DOI 10.1038/s41587-026-03331-w.
  • The immunogenicity results are in mice; neither report describes human data, a regulatory pathway or a timeline. The AI contribution is search efficiency over a five-excipient design space, not a new biological insight.
AI For ScienceDrug DiscoveryHealthcare

Sunday, 27 September 2026

Physics-informed model predicts protein-RNA binding changes from mutations at PCC 0.705 on a blind test set beneficial

Communications Biology
  • In a paper published 26 September in Communications Biology, researchers at Central China Normal University and Hainan University report that Pred-MutPRI "achieves strong generalization on a sequence-disjoint and structurally low-overlap blind test set (PCC = 0.705) and outperforms existing predictors" for mutation-induced changes in protein–RNA binding free energy.
  • The framework combines structure-derived descriptors and weighted atom-level interaction networks with two compact features: "a masked ESM-2 entropy term capturing context-dependent sequence constraint, and an AlphaFold3-derived local effective strain descriptor", fed to an XGBoost regressor.
  • The authors also introduce "thermodynamic permutation (TP) to generate cycle-consistent training pairs that expand substitution-type coverage while reducing mutation-class bias" — an attempt to work around the sparse and imbalanced measurements that the paper says make this prediction task difficult.
  • This is a peer-reviewed result, not a preprint, and the dataset and Python package are released publicly. The paper reports a correlation on a held-out set; it does not report prospective experimental validation of any specific prediction.
AI For ScienceDrug DiscoveryHealthcare

Friday, 25 September 2026

AlphaFold Database adds predicted protein complexes for more than 2,800 viruses mixedCompany claim

EMBL·NVIDIA
  • EMBL says predicted 3D structures for the protein complexes of more than 2,800 viruses are now openly available in the AlphaFold Database, which holds more than 260 million protein and protein complex predictions.
  • NVIDIA says the structures were generated with AlphaFold2 optimised by its BioNeMo Inference Runtime, that "about 30% of the protein interactions being added to the database are completely new to science", and that it is also releasing the BioNeMo Structure Prediction Pipeline used to build the dataset.
  • Partners listed by EMBL include EMBL-EBI, Google DeepMind, NVIDIA, Seoul National University, the University of Glasgow, the Swiss Institute of Bioinformatics, CEPI, Sungkyunkwan University and Lund University. Jo McEntyre of EMBL-EBI said: "Making these data open is critical for understanding viral diagnostics and developing treatments and vaccines."
  • These are predictions, not experimentally determined structures. The EMBL release does not quantify how many interactions are new; that figure comes from NVIDIA. Open viral structure data is dual-use, and neither release discusses screening or access controls.
AI For ScienceDrug DiscoveryBio RiskGoogle DeepMind

Thursday, 24 September 2026

Anthropic says Claude agents found an uncharacterised enzyme system after 21 hours and 210 million tokens beneficialCompany claimPreprint

Anthropic·TechCrunch
  • Anthropic, posting on 23 September, says Claude "autonomously discovered a novel enzyme system that is associated with an array of DNA repeats, a pattern reminiscent of CRISPR", which it names array-associated reverse transcriptases (ART). It says: "After 21 hours spent searching this data by roughly 950 agents using 210 million tokens, one of the agents spotted something remarkable: a repeating pattern of DNA sequences that occurs next to the gene for an odd-looking RT."
  • Anthropic says the agents "gathered over 200,000 RTs, picked out 3,500 new candidate systems, and narrowed those to the 20 most compelling candidates that they analyzed to produce human-readable reports". It announced a life sciences research group and lab alongside the result; all wet-lab work is done by human scientists.
  • Anthropic writes that "we don't yet know its function" and that "our work to understand the primary function of ARTs is ongoing". It notes the underlying reverse transcriptase "had been identified in previous studies". MIT and Broad Institute professor Feng Zhang is quoted saying: "The identification of RNA-repeat arrays associated with reverse transcriptases is genuinely intriguing and merits further investigation."
  • The claim is Anthropic's own and the accompanying technical report is a preprint. TechCrunch reports Dario Amodei said Stanford researchers previously found a "similar" system and that the work was done "mostly, though not entirely, by Claude". No peer-reviewed publication and no independent replication exists yet.
AnthropicAI For ScienceAgentsDrug Discovery

Nature Medicine: graph neural network scores 1.5 million Alzheimer's phenotype-associated cells across 1,494 donor brains beneficialSingle source

Nature Medicine
  • Published in Nature Medicine on 23 September, the paper describes PASCode, a Phenotype Associated Single Cell encoder that "ensembles multiple statistical methods into a graph neural model". Applied to the PsychAD dataset it "identified ∼1.5 million phenotype-associated cells (PACs) from 584 donors with AD-related phenotypes".
  • The underlying dataset is "more than 6.3 million nuclei isolated from the dorsolateral prefrontal cortex (DLPFC) of 1,494 donors". The authors compared these cells "within 27 brain cell subclasses", prioritising microglia subpopulations implicated in Alzheimer's pathology, reactive astrocyte subtypes linked to cognitive resilience, and "enhanced excitatory/inhibitory imbalance and mitochondrial dysfunction in cognitively impaired AD donors".
  • The authors say they validated findings in external population-scale datasets covering Alzheimer's disease and major depressive disorder, and released the framework as an open-source tool with pre-trained models and a web application.
  • This is cell-level association analysis in post-mortem tissue, not a clinical result. The paper reports prioritised cell populations, not a treatment, a diagnostic or a patient outcome.
AI For ScienceHealthcareDrug Discovery

Enveda raises a $311 million Series E at a $2 billion valuation for AI-discovered natural-product drugs Single source

TechCrunch
  • TechCrunch, publishing on 23 September at 19:31 UTC, reports Enveda raised a $311 million Series E at a $2 billion valuation, led by Catalio Capital Management with Iconiq and others participating. TechCrunch says the round doubles the company's valuation from 12 months earlier.
  • Enveda, founded in 2019 by Viswa Colluru, uses AI to find drug candidates derived from plants and microbes. Two candidates are in human trials: one for severe skin conditions and one designed to preserve weight loss after stopping GLP-1 medications.
  • TechCrunch notes that "AI hasn't yet produced any FDA-approved drugs". No trial results are reported, and the valuation is the company's own characterisation of the round.
Drug DiscoveryFundingHealthcare

Wednesday, 23 September 2026

Boehringer Ingelheim signs Envisagenics to an AI RNA-splicing oncology deal worth more than US$1 billion Company claimSingle source

Envisagenics
  • Envisagenics announced on 22 September at 08:00 ET that it is eligible for more than US$1 billion in potential payments from Boehringer Ingelheim, comprising an upfront payment, research funding, option fees, development, regulatory and commercial milestones, and royalties on future sales. The upfront figure is not disclosed.
  • The company says its SpliceCore platform merges AI, large-scale transcriptomics and experimental validation, and screens more than 14 million distinct splicing events to assess disease specificity, patient prevalence and therapeutic suitability. The collaboration covers antibody-drug conjugates, T-cell engagers and multispecific antibodies, with Boehringer holding an option to exclusively license selected targets.
  • Alternative RNA splicing produces tumour-specific protein variants that conventional target discovery tends to miss; Boehringer's global head of oncology research, Mark Petronczki, is quoted saying it "offers access to a largely unexplored target space".
  • The headline number is a biobucks total, not money paid: almost all of it is contingent on milestones that may never be reached, and no target, molecule or timeline is named. Nothing in the release reports a validated target, let alone a candidate drug.
Drug DiscoveryHealthcareAI For ScienceFunding

TAILORx reanalysis preprint: AI pathology model flags a subgroup gaining 5 points of 5-year disease-free interval from chemotherapy beneficialPreprintCompany claimUpdate

medRxiv
  • A preprint revision posted to medRxiv on 22 September applies Ataraxis Breast CTX, which reads H&E pathology images alongside clinical variables, to 6,735 patients from the TAILORx phase 3 randomised trial in node-negative HR+/HER2- breast cancer. The authors state no TAILORx data were used to train the model and that analyses were prespecified in a protocol approved by ECOG-ACRIN.
  • Among patients on endocrine therapy alone, the model's prognostic score predicted disease-free interval with a hazard ratio per 1 SD increase of 1.651 (95% CI, 1.511-1.803, p < 0.001) and a C-index of 0.736 (95% CI, 0.697-0.770); in the chemoendocrine group the hazard ratio was 1.642 (95% CI, 1.495-1.804, p < 0.001) with a C-index of 0.720 (95% CI, 0.681-0.756).
  • In the intermediate Recurrence Score subgroup — the group TAILORx was designed to resolve and where chemotherapy decisions are hardest — the treatment-by-biomarker interaction was significant at p = 0.001, with patients flagged as high-benefit showing "a 5% increase in observed 5-year DFI rates from the addition of chemotherapy". Stratifying the same subgroup by Recurrence Score at a threshold selecting a similar proportion yielded no significant interaction (p = 0.20).
  • This is a preprint, not peer reviewed, and a retrospective reanalysis of an existing trial rather than a new randomised test of the model. Ataraxis Breast CTX is a commercial product and the analysis is reported by its developers.
HealthcareDrug DiscoveryEvals

Tuesday, 22 September 2026

Nature: chemists prefer RetroChimera's retrosynthesis routes over published reference reactions beneficialCompany claim

Nature·Microsoft
  • The paper, published 21 September 2026, proposes "RetroChimera: a frontier retrosynthesis model, built upon two newly developed components with complementary inductive biases, integrated via a novel, learning-based ensembling strategy", and reports that "organic chemists prefer predictions from RetroChimera over published reference reactions and over other AI models".
  • It also reports "zero-shot transfer and fine-tuning on internal datasets from two major pharmaceutical companies, showing robust generalization under distribution shift" — the test that matters for industrial use, where proprietary reaction data differs from public corpora.
  • Microsoft's accompanying feature says collaborators include GSK and Novartis, that on 10 benchmark molecules RetroChimera "produced a fully accepted sequence of reactions for nine, compared with two to five for other models", and that nine PhD-level chemists preferred its approach "about 64% of the time" over previously documented routes.
  • The Nature abstract is public but the full text is paywalled, so the per-experiment detail behind the preference results was not read here. The 9-of-10 and 64% figures come from Microsoft's own feature page, which carries no visible publication date, and are not in the abstract.
Drug DiscoveryAI For ScienceMicrosoft

FDA issues direct final rule replacing "animal test" terminology and opens a database of non-animal method uses

FDA·Federal Register
  • The FDA release, dated 21 September 2026, says the direct final rule "updates its regulations to clarify that non-animal methods can be used where appropriate for testing the safety of drugs and biological products intended for human use before they're tried in humans", replacing "animal tests" and "animal studies" with "nonclinical tests" and "nonclinical studies", along with "preclinical" and "in vitro".
  • The FDA says it "also launched a database featuring specific uses of New Approach Methodologies (NAMs)", whose "initial release includes 25 examples drawn from publicly available FDA review materials" — including, per the release, methods using human cells, organs-on-chips and computer models.
  • Acting FDA Commissioner Kyle Diamantas is quoted: "This new rule supports the Trump Administration's push to explore ways to complement, or where appropriate, replace animal studies with methods that may better predict how medicines will actually affect people."
  • The FDA states the rule "does not eliminate or prohibit animal studies, change evidentiary standards or impose new costs or requirements on drug developers", and says it will withdraw the direct final rule if it receives significant adverse comments. This is terminology and a catalogue, not a validation standard for computational models.
FDADrug DiscoveryUS Federal PolicyHealthcare

Saturday, 19 September 2026

MIT preprint: Bayesian-optimisation framework produced mRNA vaccines retaining 100% bioactivity after two months at 37 °C beneficialPreprintSingle source

bioRxiv
  • The preprint, posted on 18 September with Ana Jaklenec of MIT as corresponding author, describes AGENT — Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization — which the abstract says couples high-throughput experimentation with Bayesian optimization.
  • The abstract states that manual exploration of the formulation space "required months of screening and yielded suboptimal candidates", while AGENT achieved "efficient formulation optimization in only six iterations completed within one month".
  • The resulting solid-state formulations "retained 100% bioactivity after storage at 37 degree C for over two months", and the vaccines induced antigen-specific IgG and germinal centre B cell responses the authors describe as non-inferior to freshly prepared soluble vaccines. Formulations were put into dissolvable microneedle patches and given to rodents and nonhuman primates.
  • The authors call this the first demonstration of AI-driven design of thermostable RNA vaccines. It is a preprint that has not been peer reviewed, and no human data is reported.
AI For ScienceHealthcareDrug Discovery

Anthropic confirms a Bay Area wet lab for physical biology, and says it is not specifically for drug discovery mixedCompany claim

CNBC (Reuters)·TechCrunch
  • Anthropic's head of life sciences, Eric Kauderer-Abrams, confirmed the wet lab in a Reuters interview carried by CNBC: "We believe that to do biology, the final test is still and will be for a while in real lab work." Two people familiar with the matter told Reuters the lab is in the San Francisco Bay Area.
  • CNBC reports an Anthropic spokesperson later clarified the lab is not for drug discovery specifically and declined to elaborate, and that Kauderer-Abrams said the company has "set a boundary that it is not running clinical trials for now".
  • One of the people told Reuters that Anthropic wants Claude to direct robotic units to run experiments with limited human intervention; a spokesperson said human oversight and involvement remain essential for safety. CNBC reports Anthropic bought the startup Coefficient Bio for about $400 million in stock according to media reports, which Anthropic confirmed without commenting on the price.
  • CNBC notes the lab comes in the same weeks that Anthropic researchers warned AI could lead to human extinction and the company said it found examples where its systems could have been used toward biological weapons development. Anthropic has not published the lab's biosecurity controls.
AnthropicAI For ScienceDrug DiscoveryBio RiskRobotics

Friday, 18 September 2026

Science: Stanford "Virtual Biotech" of 37,075 agents linked switch-like drug targets to a 48% higher chance of reaching market beneficial

Stanford Medicine·Nature
  • A Science paper published 17 September, "The Virtual Biotech: A multi-agent AI framework for therapeutic discovery and development" (DOI 10.1126/science.aeg6779), from a Stanford team led by Harrison Zhang with James Zou as senior author, ran a company of AI agents over the published clinical-trial literature. Nature reports a chief scientific officer agent "assigned 37,075 agents to each tackle a single later-stage trial"; Stanford says the agents catalogued some 50,000 trials in less than a week.
  • Stanford reports that drugs targeting switch-like, high-bimodality genes "were 40% more likely to advance from phase 1 to phase 2 trials, were 48% more likely to reach market, and had 32% fewer adverse events compared with those that had a broad spectrum of activity".
  • Stanford says the agents proposed an antibody-drug conjugate against B7-H3 using only information available before January 2025, and that in August 2025 a private pharmaceutical company independently arrived at the same strategy, which later received FDA breakthrough therapy designation.
  • Nature notes that "the Virtual Biotech has not been vetted in the crucible of real-world drug discovery, and its predictions were not validated through experiments, let alone clinical trials". The agents were powered by versions of Claude.
AI For ScienceDrug DiscoveryAgentsAnthropicHealthcare

Anthropic opens a Life Sciences Verification Program whose high-risk tier "removes all safeguards that block life sciences requests" mixedCompany claim

Anthropic
  • Anthropic announced on 17 September a Life Sciences Verification Program giving verified life-science organisations access to its Mythos, Opus and Sonnet models "with a refined set of safeguards more permissive for biology-related work", covering tasks "currently blocked in our generally available Fable models", including "drug discovery, research biology, clinical development, and manufacturing".
  • There are two grant types. Standard Use applies to a whole team, renews annually and covers Mythos 5.1, Opus 5 and Sonnet 5. High-risk Use is an add-on for a single research project, renews every six months, and Anthropic says "It removes all safeguards that block life sciences requests." High-risk grants for Opus 5 and Sonnet 5 are available now; Anthropic says it is working with the US government before making high-risk grants broadly available for Mythos.
  • Enforcement shifts from real-time blocking to offline monitoring of usage against each organisation's stated use cases, which Anthropic says requires 30-day data retention for flagged activity. Applicants are vetted on "research credentials, security standards, and ethical research oversight". Anthropic says it has onboarded dozens of organisations and expects "to enroll hundreds of organizations within the first week".
  • Anthropic names three early participants: Xaira Therapeutics, Edison Scientific and Manifold Bio. It cites its own threat report describing misuse attempts "that could support biological weapons development", and says cyber classifiers remain in place. No external body has reviewed the vetting criteria.
AnthropicBio RiskDrug DiscoveryHealthcare

Anthropic says Claude optimised more than 30 biomolecular models in under four weeks, roughly 4x faster on average beneficialCompany claim

Anthropic
  • Anthropic reported on 17 September that "Claude was able to optimize more than 30 deep learning models" used in biomolecular work "in just under four weeks", speeding them up "roughly 4x while sacrificing a minimal amount of precision, and nearly 2x with identical outputs".
  • It says a custom kernel set called FlashPairformer set "a new state-of-the-art, outperforming the field standard on average by 2.7-2.9x on triangle attention and 1.7-3.2x on triangle multiplication, depending on the model configuration", against NVIDIA's BioNeMo Inference Runtime as the stated baseline.
  • Anthropic says a low-memory mode allows accurate modelling of systems larger than 10,000 tokens on a single NVIDIA GPU node and successful inference on systems larger than 70,000 tokens, and that a de novo binder run matching earlier scores cost approximately $150 on GPUs and tokens against up to $10,000 per target for the earlier approach.
  • The work was supervised by two Anthropic staff who Anthropic says had biomolecular modelling experience but "no prior experience in inference optimization or kernel engineering". Every figure is Anthropic's own and none has been independently reproduced; predictions at 31,000 to 70,000 tokens "are not predicted correctly".
AnthropicAI For ScienceDrug DiscoveryCompute

Thursday, 17 September 2026

Novo Nordisk and Anthropic announce drug-discovery collaboration, starting with Claude Science in R&D workflows Company claim

Novo Nordisk
  • Novo Nordisk announced on 16 September that it and Anthropic "will jointly address key drug discovery challenges identified by scientists and computational teams at Novo, develop targeted solutions for specific scientific challenges and workflows to support biological reasoning, and help accelerate the discovery and development of new medicines".
  • The stated first step is narrow: "As an initial aim, Novo will test Claude Science for specific workflows in R&D and address scientific problems where the joint capabilities of Novo and Anthropic are expected to have the greatest impact." Novo will also use Anthropic's frontier models "to strengthen AI-driven software development".
  • Mike Doustdar, president and CEO of Novo, said the partnership is "another testament to our ambition to become the world's most AI-driven healthcare company" and that "AI can help us increase productivity in R&D and compress the path from research to marketed product". Dario Amodei said AI "brings with it the potential to compress a century's worth of biological and medical breakthroughs into a decade".
  • Neither company disclosed financial terms, a target count, a timeline or any benchmark, trial or dataset. The announcement reports no scientific result; the claims about productivity and timelines are the companies' own.
AnthropicDrug DiscoveryHealthcareAI For Science