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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.
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.