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VsNsbench: evaluating AlphaFold3-embed induced-fit mechanism for enhanced virtual screening
Shu-Kai Gu1,2,3, Chao Shen4, Yu-Wei Yang1
1Faculty of Applied Science, Macao Polytechnic University, Macao, China.
Acta Pharmacologica Sinica
|February 4, 2026
Summary
AlphaFold3 (AF3) shows improved ligand-induced modeling for virtual screening (VS) by predicting holo structures. Its performance depends on ligand affinity, highlighting potential for drug discovery but needing multi-state modeling improvements.
Area of Science:
- Computational biology
- Structural biology
- Drug discovery
Background:
- AlphaFold3 (AF3) predicts holo structures, extending AlphaFold2 (AF2).
- The induced-fit modeling capabilities of AF3 are not fully understood.
- Benchmarking AF3's performance in virtual screening (VS) is crucial.
Purpose of the Study:
- To evaluate the virtual screening performance of ligand-induced AlphaFold3 (AF3) holo structures.
- To compare AF3 holo structures against AF3 apo, experimental apo, and AlphaFold2 (AF2) structures.
- To investigate the influence of ligand affinity on AF3's induced modeling.
Main Methods:
- Benchmarking AF3 holo structures on DUD-E and VsNsBench datasets.
- Comparing enrichment capabilities of AF3 holo vs. apo structures and AF2.
- Analyzing AF3 performance based on ligand affinity and performing a kinase case study.
Main Results:
- AF3 holo structures significantly improved enrichment over AF3 apo, experimental apo, and AF2.
- AF3 performance was superior to experimental holo structures on VsNsBench but inferior on DUD-E.
- High-affinity ligands enhanced AF3's induced modeling, while low-affinity ligands resulted in poor performance.
- Direct VS with AF3 showed promise but faced computational efficiency challenges.
- AF3 successfully modeled inhibitor-specific conformations in a kinase case study (75% success rate).
Conclusions:
- AF3 effectively incorporates induced-fit modeling for predicting holo structures.
- AF3's performance is critically dependent on ligand binding affinity.
- Further improvements are needed for modeling multi-state conformational ensembles, despite promising results.
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