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Docking-based virtual screening: Past, present, and future.

Xianjin Xu¹, Xiaoqin Zou¹

¹Department of Physics and Astronomy, Dalton Cardiovascular Research Center, Department of Biochemistry, Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA.

Biophysical Journal

|April 17, 2026

Summary

This summary is machine-generated.

Docking-based virtual screening (DBVS) is crucial for identifying molecular binders for drug discovery. Recent advances enhance DBVS performance, focusing on deep learning and large-scale campaigns for improved hit identification.

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Area of Science:

Computational chemistry
Drug discovery
Structural biology

Background:

Virtual screening is vital for identifying small-molecule modulators for therapeutic targets.
Docking-based virtual screening (DBVS) has been a cornerstone of structure-based drug discovery since the 1990s.
DBVS aids in discovering molecular probes and initial drug candidates.

Purpose of the Study:

To review the components of DBVS workflows.
To summarize recent advancements in DBVS methodologies.
To discuss challenges and future opportunities in virtual screening for drug discovery.

Main Methods:

Description of DBVS workflow components: binding site identification, library preparation, and docking methods.
Summary of template-based and deep learning-based approaches for docking and scoring.
Overview of large-scale and ultra-large-scale docking campaigns.

Main Results:

DBVS workflows involve ligand-binding site identification, chemical library preparation, and molecular docking.
Recent advances include template-based methods, deep learning for docking/scoring, and large-scale screening.
The field is rapidly expanding with significant success in identifying novel modulators.

Conclusions:

Continued methodological innovation is key for DBVS.
Deep learning and large-scale campaigns are emerging trends.
Maximizing the impact of virtual screening in early-stage drug discovery is a primary goal.