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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.
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.