Keywords: active learning, Bayesian optimization, docking, drug discovery, computational chemistry
TL;DR: A tutorial on using MolPAL to accelerate high-throughput computational docking screens
Abstract: Structure-based virtual screening (SBVS) of ultra-large chemical libraries has led to the discovery of novel inhibitors for challenging protein targets. However, screening campaigns of these magnitudes are expensive and thus impractical to employ in standard practice. As the broad goal of most SBVS workflows is the identification of the most potent compounds in the library, the task can be viewed as an optimization problem. Previous work has demonstrated the ability for Bayesian optimization to improve sample efficiency in SBVS using the MolPAL software. In this tutorial, we provide a broad algorithmic overview of the MolPAL software and a guide for its utilization in a prospective virtual screening task.
Paper Track: Software & Tutorials
Submission Category: AI-Guided Design
Supplementary Material: zip