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

Abstract In early drug discovery, virtual screening—a computational method for selecting candidate compounds—helps reduce development costs. Traditionally, structure-based virtual screening required experimental protein structures, but advances like AlphaFold2 have begun to overcome this limitation. 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 inactive (decoy) ligands produce results similar to apo predictions. The use of template structures further improves 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 (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.