Structure-Based Virtual Screening: Successes and Pitfalls

Abstract

Structure-based virtual screening (SBVS) is an important approach that makes the first stages of drug development and repurposing processes faster and more efficient. Advances in experimental techniques and in silico computational modeling have contributed significantly to the characterization of diverse biological targets. Combined with the rapidly growing number of chemical compounds available in virtual databases, these advances enable the effective application of SBVS for prioritization of putative bioactive compounds to treat a wide range of pathologies. Techniques such as molecular docking, along with the utilization of pharmacophore models, are commonly employed for screening large databases of compounds, providing a solid foundation for employing SBVS in drug development pipelines. This review comprehensively analyzes recent advancements and strategies employed in the field of SBVS and explores methodologies for validation, limitations, and challenges associated with this approach. Through a series of case studies across different therapeutic targets, we demonstrate SBVS’s versatility and efficacy in identifying potential therapeutic agents. However, challenges remain, and understanding these is crucial for maximizing SBVS’s potential. We address these challenges, offering insights into the current limitations and future prospects of SBVS in drug development.