drug-discovery

This paper introduces a novel diffusion-based generative model for structure-based drug design that decouples pocket and ligand representation learning and incorporates multi-scale interaction signals and property-aware optimization to generate developable 3D molecules with improved binding affinity and ADMET properties.

This paper proposes JoPMol, a jointly controlled precision molecular generative model that integrates gene expression profiles, molecular structure text, and chemical properties to generate personalized drug candidates, outperforming state-of-the-art methods.

OpenAI researcher Miles Wang is leaving to launch an AI drug discovery startup, with talks to raise $200 million at a $2 billion valuation led by Lightspeed.

Researchers at the Technical University of Denmark used a hybrid AI and quantum computing approach to generate novel peptides that bind to specific proteins, showing improved performance especially with sparse data, potentially accelerating vaccine and personalized immunotherapy development.

This paper reveals that standard marginal conformal prediction fails to cover minority classes in imbalanced virtual screening datasets, and demonstrates that class-conditional (Mondrian) conformal prediction restores per-class coverage.

DrugGen-2 fine-tunes GPT-2 using supervised learning and reinforcement learning (GRPO) to generate small molecules conditioned on both disease ontology and target protein sequences, achieving superior diversity and binding affinity for drug discovery.

Introduces OpenDDE, an open-source all-atom biomolecular foundation model that uses co-folding as a shared structural reasoning layer for drug discovery, achieving high accuracy and identifying scaling laws for further improvement.

Active-GRPO introduces an adaptive imitation and self-improving reasoning framework that dynamically decides when to imitate references and when to reinforce the model's own discoveries for molecular optimization, achieving statistically significant improvements over previous methods on the TOMG-Bench-MolOpt benchmark.

Introduces MolSafeEval, a benchmark dedicated to evaluating safety risks in AI-generated molecules by integrating heterogeneous safety knowledge into a molecular safety knowledge graph and leveraging LLM-based reasoning for systematic detection of unsafe features.

Anthropic launched Claude Science, a new flagship product for autonomous scientific research in computational biology and drug development, available to all paid Claude subscribers.

This paper introduces an AI agent trained via reinforcement learning to reason over all FDA-approved drugs since 1939 for treatment recommendations, integrating disease context, comorbidities, and contraindications.

Google Cloud will offer SandboxAQ's large quantitative models (trained on scientific equations and lab data) for drug discovery, materials science, and semiconductor manufacturing, alongside Gemini for Science tools to accelerate research workflows.

Pepti-drift is a toxicity-aware latent refinement framework for generating antigen-specific peptides that avoids toxicity while maintaining binding affinity. It achieves large speedups over existing methods and produces diverse, valid, and low-toxicity peptide candidates.

Introduces BoBa, a bandit-guided surrogate optimization framework that eliminates full-library inference by adaptively allocating computation across partitions of the action space, enabling efficient virtual screening of ultra-large chemical libraries.

Introduces MEET, a memory-efficient E(3) equivariant transformer for full-atom peptide design, integrated with a VAE and latent diffusion pipeline to achieve linear memory scaling and improved generation quality.

Proposes two complementary approaches to incorporate predictive uncertainty into reinforcement learning for chemical language models, improving robustness and increasing true hit rate by 0.25 in de novo molecular design.

This paper introduces Sesame, a diffusion-based molecular generation model that conditions on partial molecular structure and protein pocket via spatial density maps, enabling both de novo generation and fragment-conditioned lead optimization for drug design.

TxBench-PP is a benchmark for evaluating AI agents on small-molecule preclinical pharmacology tasks. Across 16 model-harness configurations, the best system achieved only 59.3% accuracy, indicating significant room for improvement.

OpenAI and Molecule.one collaborated to have their AI systems (GPT-5.4 and Maria) autonomously select research areas, generate proposals, and run experiments in organic chemistry, achieving yield improvements for 88% of tested reactions — a first for AI-driven open-ended scientific 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.