The front page of AI-driven biology
Thursday, October 15, 2026Thu, Oct 15
Updated 8:01 PM PT
San Diego-based Iambic Therapeutics filed for an initial public offering with the US Securities and Exchange Commission to advance its pipeline of AI-designed small molecules.
Boehringer Ingelheim will use Envisagenics' SpliceCore machine-learning platform to identify and validate tumor-specific targets originating from aberrant RNA splicing, with total potential deal payments reaching $1 billion.
RetroChimera combines models with complementary inductive biases through a learned ensembling method to predict chemical synthesis pathways, outperforming leading retrosynthesis baselines across varying training data sizes.
Researchers built an active-learning system called AGENT, combining Bayesian optimization with lab testing to formulate solid-state mRNA lipid nanoparticles in six iterative rounds.
Researchers built Pep-PU-GAN, a machine learning model that pairs graph neural networks with positive-unlabeled adversarial learning to classify bioactive peptides when verified non-functional examples are scarce.
Researchers built the Neoantigen Elimination Model (NEMo), a two-compartment machine learning tool that predicts which mutant tumor targets trigger immune attack by tracking which cancer subclones disappear during immunotherapy.
Researchers introduced the Large Drug Discovery Model, a 3D generative framework that designs drug candidates to satisfy fine-grained structural goals while ensuring they can be readily made in a laboratory.
Researchers introduced latent generative search, a framework that guides the Proteina-Complexa model at inference time to generate protein sequences and 3D shapes simultaneously, eliminating the need for a separate inverse-folding step.
A computational study evaluated whether adding structural and physical rules can help three-dimensional machine learning models match conventional two-dimensional ligand-only models in predicting small-molecule binding affinity.
Researchers fine-tuned a general baseline molecular property model on small, specialized datasets to predict chemical toxicity in classes with limited historical testing.
Researchers have developed WAVE, a neural network designed to predict transcriptional changes caused by small molecules across both bulk tissue and individual cells.
Novo Nordisk has agreed to a collaboration worth up to $1.4 billion with Orbis Medicines to develop oral macrocyclic peptides for cardiometabolic diseases.
Researchers built the Virtual Biotech, an ensemble of artificial intelligence agents organized into drug development divisions including target discovery, safety assessment, modality selection, and clinical trials.
A Stanford team published an architecture in Science that coordinates specialized computational agents under a virtual chief scientific officer to analyze cross-disciplinary genomic, molecular, and clinical data.
Novo Nordisk has partnered with AI startup Anthropic to deploy its Claude model across early drug discovery and research operations.
Researchers built IGNITE, a semi-supervised machine learning framework that ranks therapeutic targets by combining human genetic evidence with Perturb-seq profiles from 22 million primary human CD4+ T cells.
Novo Nordisk plans to deploy Anthropic's Claude Science model to assist with scientific reasoning across drug research and development and agentic software tasks.
An automated spatial computing pipeline analyzed nanoscale expansion microscopy images of brain tissue from the somatosensory cortex of 5xFAD Alzheimer's disease mice.
Chinese internet company ByteDance completed a $290 million funding round for its AI-driven drug discovery division following its spin-out as an independent company.
Researchers at Imperial College London deployed three computational screening models across 6,747 small molecules to identify approved drugs that could be repurposed against the respiratory bacterium Streptococcus pneumoniae.
Using the reinforcement-learning molecular design tools Link-INVENT and AutoMolDesigner, researchers designed Y-11, a small-molecule antibiotic targeting MsbA, an essential transporter protein in Gram-negative bacterial outer-membrane assembly.
Obvious Ventures led a $20 million Series A funding round for Mithrl, with backing from Headline, AGI House, and biopharma executives, to advance its biomedical world model and AI orchestration platform.
A computational framework named E-MAF combines manufacturing process predictions with expert-mimic evolutionary search to design controlled-release drug formulations from target dissolution profiles.
ProtScape combines global protein interaction data, single-cell gene expression, and protein language models to reconstruct context-specific protein interactomes across more than 200 cell types.
Researchers released HARIBOSS++, an updated structural database and web platform cataloging small molecules that bind RNA.
Computational chemists introduced ShEPhERD-2, a 3D generative model that builds drug candidates directly from physical interaction profiles specifying shape, electrostatics, and chemical binding features called pharmacophores.
Researchers introduced QuantaMind, a machine-learning force field designed to simulate bond-breaking and bond-forming reactions with density functional theory accuracy.
UniCure links biological and chemical foundation models to forecast drug-induced gene expression changes, aiming to bridge the gap between cancer cell lines and clinical responses.
A proximity-guided graph learning approach mapped tumor-associated cell-surface protein communities to uncover targetable membrane proximity antigens.
Scientists developed ProteinTalks, an operational virtual cell model pretrained on more than 38 million time-resolved protein abundance measurements from perturbed breast cancer cell lines.