Deep Learning-assisted Pipeline for Virtual Screening of Ligand Compound Databases: Application on Inhibiting the Entry of SARS-CoV-2 into Human Cells

Abstract

Drug discovery involves extremely costly and time consuming procedures and can be significantly benefited by computational approaches, such as virtual screening (VS). Structure-based VS relies on scoring functions which aim to evaluate the binding of a candidate compound (ligand) on a protein target. Over the last few years, the advancement of the deep learning field has led to the development of novel scoring functions based on convolutional neural networks (CNN), which have achieved state-of-the-art results. In this paper, we present an integrated end-to-end VS pipeline for application on real-world drug discovery scenarios. It combines multiple conformations of the ligand with a new CNN scoring function based on the ResNet architecture, called ResNetVS, which incorporates also the docking output score in its evaluation. After experiments on the DUD-E dataset, it has shown notable performance, especially in early enrichment, where it overcomes current benchmarks. The proposed pipeline is finally applied on the emerging case of COVID-19 pandemic, in a struggle to discover inhibitors for the viral spike protein-ACE2 interaction.