EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation

arXiv:2610.36280v1 Announce Type: new Abstract: Molecular 3D structures underpin simulations of thermodynamics, kinetics, crystallization, and nucleation, making their accurate prediction central to drug discovery and materials design. However, molecular geometry can change dramatically across environments. Existing models either ignore environmental effects or train separate models for each environment. We hypothesize that transfer learning across contexts is possible for conformer generation and that a general model will perform better when training data are limited, as in ligand binding and crystallization.