Virtual screening: hope, hype, and the fine line in between.

Abstract

Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.

Areas covered: This perspective examines the current VS landscape, highlighting essential practices and identifying critical challenges, limitations, and common pitfalls. Using case studies and practices, this perspective aims to highlight strategies that can effectively mitigate or overcome these challenges. Furthermore, the perspective explores common approaches for addressing pharmacodynamic and pharmacokinetic issues in optimizing VS hits.

Expert opinion: VS has become a tried-and-true technique of drug discovery due to the rapid advances in computational methods and machine learning (ML) over the past two decades. Although each VS workflow varies depending on the chosen approach and methodology, integrated strategies that combine biological and in silico data have consistently yielded higher success rates. Moreover, the widespread adoption of ML has enhanced the integration of VS into the drug discovery pipeline. However, the absence of standardized evaluation criteria hinders the objective assessment of VS studies' success and the identification of optimal adoption methods.