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Electrostatic embedding can dramatically enhance binding free energy predictions for specific targets, but its effectiveness varies significantly across different systems.
Generate novel drug candidates with favorable binding properties by steering a chemical language model with contrastive embeddings of protein-ligand complexes.
A 40x speedup in conformer generation unlocks accurate, thermodynamics-informed pKa prediction and protonation state assignment for drug discovery.
