drug-discovery

A collaboration between Ångström AI and AstraZeneca introduces CSP-MACE-Å, a machine learning interatomic potential that aims to replace DFT in crystal structure prediction, achieving comparable accuracy at much lower computational cost.

Onepot AI can synthesize and deliver custom molecules in just 5 days by combining robotic synthesis with large-scale ML inference on Anyscale, dramatically accelerating drug discovery.

TRACE is a trajectory-aware LLM agent for molecular lead optimization that uses sequential decision-making over molecular optimization tools, achieving improved ADMET properties while preserving molecular similarity.

Introduces InteractBind, a large-scale dataset and benchmark for fine-grained evaluation of protein-ligand models, focusing on binding-site localization and non-covalent interaction prediction. Evaluates eight existing models and finds limited binding-site localization despite strong binary binding prediction.

A GitHub repository called scientific-agent-skills contains 138 Agent Skills for scientific research tools, covering bioinformatics, drug discovery, clinical databases, and more. It supports one-line integration into AI agents, providing precise API calling methods.

A roundup of 12 AI co-scientist systems in 2026, including DeepMind's Co-Scientist finding a fibrosis drug candidate and OpenAI's reasoning model solving an 80-year-old geometry problem, highlighting open-source tools for biology, fluid simulation, and automated research.

AI is transforming U.S. pharmaceutical R&D by accelerating drug discovery, preclinical testing, clinical trials, and regulatory review, with early evidence suggesting it could cut development times by roughly half and reduce costs.

A comprehensive open-source collection of 138 scientific agent skills that transform AI coding assistants like Claude Code and Codex into AI scientists, covering biology, chemistry, medicine, and more, with integration of over 100 scientific databases and specialized Python packages.

A critical analysis of Google DeepMind CEO Demis Hassabis's claim at Google I/O that AI could 'solve all diseases,' contextualizing the potential of tools like AlphaFold and AlphaGenome while highlighting the gap between scientific promise and public perception.

A recorded March 2026 conversation with Demis Hassabis covering AI's hardest solved problems, drug discovery, military use, human uniqueness, and his legacy.

MIT Associate Professor Connor Coley discusses his work developing AI models to understand chemical principles and accelerate drug discovery by predicting reaction pathways and analyzing vast numbers of potential compounds.

AI systems using teams of agents, like Google's Co-Scientist and FutureHouse's Robin, can accelerate drug repurposing research by developing hypotheses, proposing experiments, and analyzing data in hours instead of months.

SandboxAQ has integrated its large quantitative models (LQMs) for drug discovery and materials science into Anthropic's Claude, enabling researchers to use these powerful tools through a natural language interface without specialized computing infrastructure.

DrugSAGE is a framework that accumulates and reuses cross-task memory to build state-of-the-art drug discovery models efficiently, outperforming baseline agents by 10-30% on held-out tasks.

Proposes CoMole, a controllable molecular generative foundation model using motif-aware graph diffusion and reinforcement learning, achieving superior controllability across materials and drug discovery benchmarks.

DeepMind's Co-Scientist AI system helped researchers at the University of Edinburgh generate a novel, experimentally verified hypothesis linking the NLRP3 inflammasome to the mechanism of drug resmetirom in MASH liver disease, potentially enabling targeted combination therapies.

This GitHub repository provides 135 ready-to-use scientific AI skills covering biology, chemistry, medicine, and other fields. They can be integrated into AI agents with one click to accelerate research workflows.

This paper introduces SCOPE-Bench, a benchmark for evaluating molecular out-of-distribution generalization, and POMA, a framework using reinforcement learning to select source domains for domain adaptation, achieving significant error reductions on 3D molecular models.

MAML is a novel multi-modal AI model that unifies understanding of chemistry, genetics, and proteins, outperforming specialized models on 11 drug discovery benchmarks, promising to accelerate pharmaceutical research and improve success rates.

Demis Hassabis, co-founder of Google DeepMind, announces $2.1 billion in funding for Isomorphic Labs to use AI to cure all diseases by accelerating drug discovery.