The front page of AI-driven biology
Thursday, October 15, 2026Thu, Oct 15
Updated 6:32 PM PT
Researchers compared the specialized splicing model OpenSpliceAI against the genomic foundation model AlphaGenome to predict rare, disease-linked RNA splicing errors called cryptic exons caused by TDP-43 dysfunction.
GlaxoSmithKline has agreed to access Chai Discovery's AI models for protein folding and design after validating generated antibody candidates across an internal panel of drug targets.
Researchers combined a phenotypic screen of around 1,200 approved drugs with Boltz-2 structural modeling to identify inhibitors of NALCN, a sodium leak channel mutated in the ultra-rare neurodevelopmental condition CLIFAHDD.
Researchers combined two-dimensional melanoma cell cultures with mechanical particle simulations to study how spatial crowding drives drug-resistant tumor cells to break free.
Alphabet spin-out Isomorphic Labs is in preliminary discussions to raise external capital at a valuation of at least $40 billion, according to reporting from Bloomberg.
Researchers developed PK-MUSE, a computational model that marries traditional two-compartment physiological equations with neural networks to predict human drug concentration over time directly from chemical structure.
Roche has entered a research collaboration with Chinese startup Defand Therapeutics to discover and develop molecular glue drugs for cancer and immune diseases.
Researchers built a deep-learning model called InfluProto that evaluates the cross-species transmission risk of influenza A by framing host adaptation on a continuous spectrum rather than as rigid categories.
The computational drug design startup spun out of Google DeepMind is negotiating a fresh financing round that could value the company between $40 billion and $50 billion.
Researchers built Hyper Screening X, a generative flow network that bakes reaction rules directly into design algorithms to search an 11-trillion make-on-demand compound library while scoring only 10 million candidates.
The San Diego biotech plans to offer 9.4 million shares at $15 to $17 each, targeting roughly $135 million in net proceeds to fund clinical trials through 2028.
Life sciences equipment giant Danaher plans to launch its first autonomous research lab run by AI by early 2027, according to Reuters reporting.
The Advanced Research Projects Agency for Health awarded Bexorg up to $28.3 million, reported by Endpoints News as $28 million, over a three-phase, 36-month contract.
Nextech Invest and Norwest co-led the round, joined by Sanofi Ventures, Digitalis Ventures, Invus, BB Biotech, the T1D Fund, and BOLD Longevity Growth.
Comparative analysis of four field strains of Colletotrichum graminicola against a reference assembly revealed extensive chromosomal rearrangements, duplications, and non-conserved minichromosomes.
Researchers designed Neural Petri Flow, an architecture that embeds physical valence and atom-conservation laws as fixed mathematical layers while using neural networks only to predict reaction propensities.
Researchers built MAGI, an open modular agent designed to plan molecular optimization, launch generative tools, and interpret structure-activity relationships across drug discovery campaigns.
Researchers built a scoring system called semantic precision score using the biomedical language model BioLORD to weed out overly broad answers, such as general terms like antibiotics or pharmaceutical preparations, from knowledge graphs querying potential treatments for 200 diseases.
Researchers built a deep-learning framework that embeds directed causal graphs mined from toxicology literature directly into predictive models, mapping chemical structures to multi-target perturbation profiles.
Researchers developed HALO, a machine-learning framework trained on 3,160 drug-pair-strain interactions across 97 antibacterial compounds and 10 bacterial strains to predict whether pairs of antibiotics work better together.
Machine-learning analysis of temporal changes across the cell-surface proteome identified the enzyme ALPP as an adaptive target that pancreatic tumors produce in response to KRAS inhibition.
Xaira Therapeutics unveiled X-Design, its generative artificial intelligence platform built to design drug-ready antibodies from scratch, along with two early preclinical pipeline candidates.
An analysis of approximately 2 million PubChem bioassays found that more than 99.9% lack standardized ontology tags for assay formats and detection technologies, creating major hurdles for training molecular property models.
Researchers designed an algebraic counting field model to detect cryptic binding pockets, which are hidden drug-binding sites that only become visible when a ligand attaches to a protein.
Seattle-based Talus Bioscience has unveiled Ptarmigan-1, an AI tool designed to predict small-molecule binding sites directly across disordered and unstructured human proteins.
Researchers introduced RetroGEF, a flow-based generative machine learning model that predicts single-step chemical retrosynthesis routes to produce target small molecules.
Researchers combined high-throughput yeast display screening with AlphaFold3 structural modeling to discover single-domain antibodies that neutralize neurotoxins from multiple snake species.
Researchers introduced CrystAF, an all-atom flow-matching model paired with the UMA interatomic potential, to evaluate the best stage to inject physics into generative models predicting molecular crystals.
Researchers developed PRIORITI, a computational framework that uses a domain-instructed large language model to synthesize genetic evidence into predictive representations for drug target and disease pairs.
Baylor College of Medicine researchers paired unbiased high-throughput proteomics with a computational pipeline named GluePlex to discover and optimize small-molecule degraders of VAV1, an immune signaling protein involved in autoimmune disorders and blood cancers.