Convolutional Neural Networks for Predicting Molecular Binding Affinity to HIV-1 Proteins
Paul Morris, Yahchayil DaSilva, Evan A. Clark, William Edward Hahn, Elan Barenholtz · 2018
Computational techniques for binding-affinity prediction and molecular docking have long been considered in terms of their utility for drug discovery. With the advent of deep learning, new supervised learning techniques have emerged which can utilize the wealth of experimental binding data already available. Here we demonstrate the ability of a fully convolutional neural network to classify molecules from their Simplified Molecular-Input Line-Entry System (SMILES) strings for binding affinity to HIV proteins. The network is evaluated on two tasks to distinguish a set of molecules which are experimentally verified to bind and inhibit HIV-1 Protease and HIV-1 Reverse Transcriptase from a random sample of drug-like molecules. We report 98% and 93% classification accuracy on the respective tasks using a computationally efficient model which outperforms traditional machine learning baselines. Our model is suitable for virtually screening a large set of drug-like molecules for binding to HIV or other protein targets.