3D Deep Learning for Biological Function Prediction from Physical Fields

Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. The electron density field and electrostatic potential field of a molecule contain the "raw fingerprint" of how this molecule can f...

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Bibliographic Details
Main Authors: Golkov, Vladimir, Skwark, Marcin J., Mirchev, Atanas, Dikov, Georgi, Geanes, Alexander R., Mendenhall, Jeffrey, Meiler, Jens, Cremers, Daniel
Format: Conference Proceeding
Language:English
Subjects:
Amino acids
Atomic measurements
Compounds
computational biology
deep learning
drug discovery
Electric potential
Electrostatics
protein function prediction
Proteins
QSAR
Three-dimensional displays
virtual screening
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Summary:Predicting the biological function of molecules, be it proteins or drug-like compounds, from their atomic structure is an important and long-standing problem. 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 experiment, the activity of small molecules is predicted with quality comparable to state-of-the-art descriptor-based methods. 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.
ISSN:2475-7888
DOI:10.1109/3DV50981.2020.00103