Ultra‐Large Virtual Screening: Definition, Recent Advances, and Challenges in Drug Design

Abstract

Virtual screening (VS) in drug design employs computational methodologies to systematically rank molecules from a virtual compound library based on predicted features related to their biological activities or chemical properties. The recent expansion in commercially accessible compound libraries and the advancements in artificial intelligence (AI) and computational power – including enhanced central processing units (CPUs), graphics processing units (GPUs), high‐performance computing (HPC), and cloud computing – have significantly expanded our capacity to screen libraries containing over 109 molecules. Herein, we review the concept of ultra‐large virtual screening (ULVS), focusing on the various algorithms and methodologies employed for virtual screening at this scale. In this context, we present the software utilized, applications, and results of different approaches, such as brute force docking, reaction‐based docking approaches, machine learning (ML) strategies applied to docking or other VS methods, and similarity/pharmacophore search‐based techniques. These examples represent a paradigm shift in the drug discovery process, demonstrating not only the feasibility of billion‐scale compound screening but also their potential to identify hit candidates and increase the structural diversity of novel compounds with biological activities.