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Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4
- Source :
- Journal of Computer-Aided Molecular Design, Journal of Computer-Aided Molecular Design, Springer Verlag, 2020, 34, pp.191-200. ⟨10.1007/s10822-019-00263-3⟩, Journal of Computer-Aided Molecular Design, 2020, 34, pp.191-200. ⟨10.1007/s10822-019-00263-3⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- International audience; The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.
- Subjects :
- Macrocyclic Compounds
Computer science
Protein Conformation
Machine learning
computer.software_genre
Ligands
01 natural sciences
Autodock vina
0103 physical sciences
Drug Discovery
Humans
Physical and Theoretical Chemistry
Databases, Protein
Binding Sites
010304 chemical physics
business.industry
Proteins
0104 chemical sciences
Computer Science Applications
Molecular Docking Simulation
010404 medicinal & biomolecular chemistry
Protein–ligand docking
13. Climate action
Docking (molecular)
Drug Design
Pose prediction
Artificial intelligence
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
business
computer
Software
Protein Binding
Subjects
Details
- Language :
- English
- ISSN :
- 0920654X and 15734951
- Database :
- OpenAIRE
- Journal :
- Journal of Computer-Aided Molecular Design, Journal of Computer-Aided Molecular Design, Springer Verlag, 2020, 34, pp.191-200. ⟨10.1007/s10822-019-00263-3⟩, Journal of Computer-Aided Molecular Design, 2020, 34, pp.191-200. ⟨10.1007/s10822-019-00263-3⟩
- Accession number :
- edsair.doi.dedup.....60c108044945af071be75fce2f71d15a
- Full Text :
- https://doi.org/10.1007/s10822-019-00263-3⟩