ArticleJournal of chemical information and modeling2025
PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Data-driven enhanced sampling of mechanistic pathways.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Symmetric Ligand Binding Pathways and Dual-State Bottleneck in [NiFe] Hydrogenases from Unbiased Molecular Dynamics.The journal of physical chemistry letters · 2025Article
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Authors and funding
3 authors.
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Abstract
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. To expedite the examination of MD simulations, we constructed PathInHydro, a set of supervised ML models capable of automatically assigning unbinding pathways for the dissociation of gas molecules from [NiFe] hydrogenases, using the unbinding trajectories of CO and H
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Registered trials
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