AI RESEARCH
A DNN Biophysics Model with Topological and Electrostatic Features
arXiv CS.LG
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ArXi:2409.03658v3 Announce Type: replace In this project, we present a deep neural network (DNN)-based biophysics model that uses multi-scale and uniform topological and electrostatic features to predict protein properties, such as Coulomb energies or solvation energies. The topological features are generated using element-specific persistent homology (ESPH) on a selection of heavy atoms or carbon atoms. The electrostatic features are generated using a novel Cartesian treecode, which adds underlying electrostatic interactions to further improve the model prediction.