AlphaFold3 for Structure-guided Ligand Discovery

Deep-learning methods for protein structure prediction, such as AlphaFold2 (AF2) and RosettaFold (RF), have transformed structural biology and accelerated downstream biological discovery. More recent models, including AlphaFold3 (AF3) and RosettaFold All-Atom (RFAA), extend this capability to protein-ligand co-folding, enabling direct prediction of bound complexes from sequence and ligand inputs. This advance has raised the possibility that such models could function not only as structure predictors but also as AI-based molecular docking engines for virtual screening. Yet their true impact on ligand discovery remains unclear and, in many cases, controversial. Here, we systematically assess AF3 co-folding across tasks central to small-molecule discovery compared to conventional molecular docking. First, retrospective enrichment of actives over decoys showed that AF3 outperforms the physics-based docking program DOCK3 across 43 drug targets in DUDE-Z; however, this advantage is largely driven by hidden ligand-only biases inherent to computational decoy sets. In contrast, in three large experimental datasets (the sigma-2 receptor ({sigma}2), the D4 dopamine receptor (D4), AmpC {beta}-lactamase (AmpC)) with over 2,500 tested molecules that lack such biases, DOCK3 achieved stronger overall enrichment, while AF3 contributed mainly to early enrichment. Second, out-of-sample pose reproduction on >8,000 protein-ligand complexes deposited after the AF3 training and validation cutoff showed that AF3 accuracy is strongly dependent on training-set similarity, indicating that the model memorizes atomic positions more than learning general principles of molecular recognition. Finally, in the first prospective head-to-head screen against the {sigma}2 receptor, novel to AF3s training set, AF3 achieved a 13% hit rate and identified a 13 nM binder directly from the screen. However, compared with the parallel DOCK3 campaign, AF3 delivered a two-fold lower hit rate, despite yielding a similar affinity distribution among the top five hits. AF3 therefore represents the beginning of deep-learning-based structure-guided ligand discovery: a complementary tool rather than a replacement for conventional docking, with practical applications both as a screening engine and as a post-docking filter that improves hit rates. More broadly, this work establishes a framework for evaluating next-generation deep-learning co-folding models and quantifying their impact on small-molecule discovery.