Three-Dimensional Molecular Representations for Deep Learning of Bioactivities

13th Women in Machine Learning Workshop at NeurIPS 2018 | Montreal, QC

Date: December 3, 2018

Speaker: Amanda Li, PhD

Authors: Amanda Li [1, 2], Seth Axen [2], Eric Stahlberg [1], Michael Keiser [2]

1 Accelerating Therapeutics for Opportunities in Medicine (ATOM), Frederick National Laboratory for Cancer Research

2 University of California, San Francisco

Title: Three-Dimensional Molecular Representations for Deep Learning of Bioactivities

Abstract: In recent years, deep learning has been increasingly applied to pharmaceutical research and drug discovery. However, to train a deep neural network to predict molecular properties, the input representations of molecular structure must invariably be reduced to one-dimensional vectors, or fingerprints, that are equal in length for all training examples. Two-dimensional (2D) molecular fingerprints are widely used, but there are inherent limitations to the similarity patterns they are able to relate. On the other hand, fingerprints which consider three-dimensional (3D) molecular shape must address the challenges of representing the multi-state and dynamic nature of molecules. In this work, we evaluate a 3D representation of molecular structure (E3FP) against its 2D counterpart (ECFP) in multi-task prediction of drug-target bioactivities, and we assess multiple strategies for incorporating multiple 3D molecular conformations, including those that take Boltzmann weighting into account. We compare the performance of these representations in training deep neural networks using publicly-available bioactivity data (ChEMBL, DrugMatrix).