drug-discovery

GPT-5.4, in collaboration with Molecule.one's Maria AI platform, autonomously drove a medicinal chemistry project from literature review to validated experimental result, proposing an unexpected improvement to a widely used reaction in drug discovery.

OpenAI connected GPT-5.4 to an autonomous chemistry AI (Maria) to improve Chan-Lam coupling of primary sulfonamides, achieving significant yield improvements in medicinal chemistry reactions.

OpenAI introduces LifeSciBench, a benchmark of 750 expert-authored tasks to evaluate AI systems on realistic life science research workflows, including evidence handling, analysis, and scientific reasoning.

A study presenting a cross-method explainability audit of the BridgeDPI drug-target interaction model, combining gradient-based attributions and occlusion to reveal modality dominance and artifacts, providing testable hypotheses for drug discovery.

This paper proposes CPES, a curvature-informed potential energy surface graph neural network for protein-ligand binding affinity prediction. It integrates physics-informed curvature representations to model conformational flexibility and achieves improved predictive performance on benchmark datasets.

This paper proposes RicciBind, a geometric representation framework that integrates Ricci curvature and optimal transport for protein-ligand binding affinity prediction, demonstrating superior accuracy and interpretability across benchmarks.

APCyc is a target-aware generative framework that designs cyclic peptides with controlled physicochemical properties by explicitly modeling cyclization patterns and using Bayesian posterior guidance.

MDForge is an LLM agent that automates the design of molecular dynamics pipelines for host-guest binding free-energy calculations, achieving human-expert competitive results and discovering a novel high-affinity binder.

This paper proposes a probabilistic contrastive pretraining framework for molecular graph transformers to improve multi-task ADME property prediction in drug discovery, achieving significant gains on three benchmarks.

This paper introduces GLACIER, a multimodal student-teacher foundation model that integrates molecular graphs, SMILES strings, and physicochemical descriptors to predict molecular properties efficiently. It leverages Finsler geometry-aware fusion and knowledge distillation from larger teacher models (MiniMol, MolFormer) to achieve high performance with a lightweight architecture.

TamarindBio has been selected to build, host, and operate the inference infrastructure layer for TuneLab 2.0, Eli Lilly's collaborative AI/ML drug discovery platform.

A world-first vaccine has been designed using Artificial Intelligence, marking a significant milestone in the application of AI to medical and pharmaceutical development.

OpenAI introduces an updated GPT-Rosalind model purpose-built for life sciences research, with improved performance in medicinal chemistry, genomics, and drug-discovery workflows, and new benchmarks like LifeSciBench and MedChemBench.

CP-Agent is a multimodal large language model that interprets cellular morphological changes under chemical perturbations using context-aware alignment (CP-CLIP), enabling interpretable and scalable phenotypic screening for drug discovery.

GoogleDeepMind collaborated with global scientific experts to evaluate an AI system that identified new targets for liver fibrosis and fresh approaches to ALS, digesting decades of research.

This paper introduces PROBE, a framework that uses LLM agents to iteratively optimize ligands in structure-based drug design by probing pocket-ligand complex responses before editing, achieving state-of-the-art results on CrossDocked2020.

ATOM is a multi-agent framework that formulates molecular optimization as a tree-structured search with specialized agents along paths, enabling exploration of alternative molecular trajectories and improving Pareto coverage in multi-objective benchmarks.

Daraxonrasib received a standing ovation at ASCO as Revolution Medicines' breakthrough against pancreatic cancer, celebrated by over 40,000 oncologists, entrepreneurs, investors, and patient advocates.

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.