Generation of appropriate protein structures for virtual screening using AlphaFold3 predicted protein–ligand complexes

In early drug discovery, virtual screening—a computational method for selecting candidate compounds—helps reducing development costs. Traditionally, structure-based virtual screening relied on experimental protein structures or homology models. Recent advances, such as AlphaFold2, have improved the accessibility and accuracy of structure prediction, helping to overcome limitations in target structure availability. However, AlphaFold2 does not capture ligand-induced conformational changes (transition from apo to holo forms), limiting its utility for protein–ligand docking. In this study, we evaluate AlphaFold3, which predicts protein–ligand complex structures when both protein and ligand inputs are provided. Using the DUD-E dataset and Uni-Dock, we show that holo structures predicted with ligand inclusion yield higher screening performance than apo structures generated without ligand input. Notably, incorporating active ligands enhances screening performance, whereas decoys produce results similar to apo predictions. The use of experimentally determined template structures as references in AlphaFold3 further improves prediction outcomes. We also analyze the impact of ligand molecular weight, binding pocket location, and AlphaFold3 ranking scores on screening performance. Our findings indicate that lower molecular weight ligands tend to generate predicted structures that more closely resemble experimental holo structures, thus improving screening efficacy. Conversely, larger ligands with molecular weights in the range of 700–800 can induce open binding pockets that favor screening for some targets. These results suggest that employing AlphaFold3 with appropriate ligand inputs is a promising strategy for virtual screening, particularly for proteins lacking experimental structural data. • Evaluation of holo structure prediction by AlphaFold3 for structure-based virtual screening using Uni-Dock. • Comparison of without ligand, co-crystallized complex ligand, active ligand, and decoy inputs to AlphaFold3. • Active ligand input improved prediction accuracy and docking results, while decoy input performed similarly to apo. • Appropriate ligand selection enhances virtual screening outcomes, supporting the use of AlphaFold3 for structure generation.