High-throughput binding free energy simulations: Applications in drug discovery.

Abstract

Computational drug discovery methods are becoming more and more prevalent in the drug discovery pipeline. Binding free energy (BFE) simulations are a class of molecular dynamics-based free energy methods that utilize a nonphysical “alchemical” thermodynamic pathway to efficiently and rigorously compute protein-ligand complex binding free energies. With the advent of GPU computing, BFE simulations can be performed on a timescale reasonable for small molecule drug discovery applications. Combining these methods with machine learning methods such as active learning, one can rapidly explore large chemical spaces with a fraction of the cost of brute force simulations. Herein, we describe a framework for the high-throughput and highly automated screening of a large number of chemically diverse small molecule ligands for the LRRK2 WDR domain using BFE calculations with thermodynamic integration. LRRK2 is the most commonly mutated gene in familial Parkinson's Disease, and its WDR domain is an understudied drug target with no known molecular inhibitors. This work was done as a submission to the Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge. We also discuss various methods to minimize computational cost while maintaining accuracy and make recommendations for future high-throughput screening campaigns.