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Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures
Open accessPreprint — published as 10.1021/acs.jcim.2c01270
Abstract
Machine learning protein structure prediction, such as RosettaFold and AlphaFold2, have impacted the structural biology field, raising a fair amount of discussion around its potential role in drug discovery. While we find some preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a target with low sequence identity. In order to address this, we have developed an AlphaFiold2 version where we exclude all structural templates with more than 30% sequence identity. In a previous study, we used those models in conjunction with state of the art free energy perturbation methods. In this work we focus on using them in rigid receptor ligand docking. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario; one might think in including some post processing modeling to drive the binding site into a more realistic holo target model.
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Highly accurate protein structure prediction with AlphaFold
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Metadata
- Authors
- Díaz-Rovira, A. M., Martín, H., Beuming, T., Díaz, L., Guallar, V., & Ray, S. S.
- Affiliations
- Barcelona Supercomputing Center (ES)
- Corresponding author
- Ray, Soumya S.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- openRxiv
- Published
- 2022-08-18
- Volume
- 63
- Issue
- 6
- Pages
- 1668-1674
- ISSN
- 1549-9596 (ISSN-L)
- OpenAlex
- W4323652503
- Cited by
- 95
- References
- 1
- Citations by year
- 2026: 20 · 2025: 17 · 2024: 42 · 2023: 14 · 2022: 2
- FWCI
- 13.33
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Funders
- Ministerio de Ciencia e Innovación (FPU21/03921, PTQ2018-009991)
- Agencia Estatal de Investigación (AEI//10.13039/501100011033/, 501100011033, 10.13039/501100011033, MCIN/AEI/10.13039/501 100 011 033, 13039, .13039/501100011033, 10.13039)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- AlphaFold2, virtual screening, protein-ligand docking, protein structure prediction, drug discovery, hit identification
- MeSH terms
- Deep Learning, Algorithms, Ligands, Protein Binding, Protein Conformation, Proteins, Molecular Docking Simulation
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