Abstract

Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem in biology and medicine. The electron density field and electrostatic potential field of a molecule contain the “raw fingerprint” of how this molecule can fit to binding partners. In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields. Protein function based on Enzyme Commission numbers is predicted from the approximated electron density field. In another set of experiments, the activity of small molecules is predicted with quality comparable to state-of-the-art descriptor-based methods, meaning that neural networks are able to extract the relevant information from raw physical fields, without using handcrafted descriptors. We propose several alternative computational models for the GPU with different memory and runtime requirements for different sizes of molecules and of databases. We also propose application-specific multi-channel data representations.

Full research paper available here: 3D_Deep_Learning_for_Biological_Function_Prediction_from_Physical_Fields