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S1-Omni is a unified multimodal reasoning model for scientific tasks including understanding, prediction, and generation. It is trained on a corpus of 200 scientific tasks and outperforms GPT-5.5 and Gemini-3.1-Pro on most benchmarks.
Introduces HEDGEHOG, a hierarchical benchmark for evaluating molecular generative models in drug discovery, revealing that only 0.65% of generated molecules pass all medicinal chemistry and docking filters.
SinAE introduces a single-architecture flow-matching autoencoder using vanilla Transformers that achieves near-lossless reconstruction across molecules, crystals, and proteins, enabling cross-domain training and strong generative performance on standard benchmarks.
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 introduces Reward Transport, a method that uses optimal transport coupling during flow matching training to align a scalar noise coordinate with molecular rewards, enabling monotone control over molecular properties like logP and QED at inference without additional computation.
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 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.
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.
This paper introduces TopVAE, a topology-optimized VAE that reduces 'dark areas' in molecular latent diffusion by making the decoder internalize structural and chemical constraints, achieving significant improvements in molecular generation quality.
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.
ToolMol is an evolutionary agentic framework that combines a multi-objective genetic algorithm with an LLM-based operator to design small-molecule drugs, achieving state-of-the-art binding affinity and drug-likeness on multiple protein targets.