ArticleJournal of computer-aided molecular design2019
Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MELD in Action: Harnessing Data to Accelerate Molecular Dynamics.Journal of chemical information and modeling · 2025Review
- Molecular mechanism of interactions between ACAD9 and binding partners in mitochondrial respiratory complex I assembly.iScience · 2021Article
- D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.Journal of computer-aided molecular design · 2020Article
- Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Correlation of cathepsin S with coronary stenosis degree, carotid thickness, blood pressure, glucose and lipid metabolism and vascular endothelial function in atherosclerosis.Experimental and therapeutic medicine · 2020Article
- Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.Journal of computer-aided molecular design · 2019Article
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Authors and funding
13 authors.
Funding
Abstract
Manifold representations of rotational/translational motion and conformational space of a ligand were previously shown to be effective for local energy optimization. In this paper we report the development of the Monte-Carlo energy minimization approach (MCM), which uses the same manifold representation. The approach was integrated into the docking pipeline developed for the current round of D3R experiment, and according to D3R assessment produced high accuracy poses for Cathepsin S ligands. Additionally, we have shown that (MD) refinement further improves docking quality. The code of the Monte-Carlo minimization is freely available at https://bitbucket.org/abc-group/mcm-demo .
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