Author

Hanyu Chen

Beckman Research InstituteORCID 0000-0002-5648-9939Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Molecular Biology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Approach:machine learning (classical)· 2 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Approach:mixed model· 1 Assay:protein structure· 1 Research Focus:software / tool· 1

Frequent Co-Authors

Andréi S. RodiniD· 3 shared Nagarajan VaidehiiD· 3 shared Sergio BranciamoreiD· 3 shared Elizaveta MukhalevaiD· 2 shared Stéphane A. LaporteiD· 2 shared Wijnand J. C. van der VeldeniD· 2 shared Yoon NamkungiD· 2 shared Aaron Cho· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 38 mutual co-author links

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3 shared papersPharmacology & Toxicology

Andréi S. RodinORCID

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Author profile →

3 papers

Recent·Most cited

Allosteric pathways govern Gα protein coupling selectivity at promiscuous GPCRs

Tegvir S Boora, Hanyu Chen, Aaron Cho, Wijnand J. C. van der Velden, Yoon Namkung, Yubo Cao, Jay Y. Paranjpe, Franziska M. Heydenreich, Andréi S. Rodin, Sergio Branciamore, Nagarajan Vaidehi, Stéphane A. Laporte

The paper identifies allosteric pathways that determine G protein coupling selectivity in promiscuous GPCRs using a combination of mutagenesis, simulations, and machine learning.

bioRxiv · Sep 3, 2026 · PreprintPharmacology & Toxicology

Interpretable Machine Learning Model of Receptor Dynamics Reveals AT1R Allostery and a Negative Allosteric Modulator

Hanyu Chen, Yoon Namkung, Zahra Asadi Jafari, Wijnand J. C. van der Velden, Elizaveta Mukhaleva, Grady Kestler, Andréi S. Rodin, Sergio Branciamore, Stéphane A. Laporte, Nagarajan Vaidehi

The paper develops an interpretable machine learning model to identify allosteric modulators of the AT1R receptor and discovers a negative allosteric modulator.

bioRxiv · Aug 3, 2026 · PreprintMolecular Biology

BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories

Elizaveta Mukhaleva, Babgen Manookian, Hanyu Chen, Indira R. Sivaraj, Ning Ma, Wenyuan Wei, Konstancja Urbaniak, Grigoriy Gogoshin, S. Bhattacharya, Nagarajan Vaidehi, Andréi S. Rodin, Sergio Branciamore

BaNDyT uses Bayesian network modeling to analyze molecular dynamics trajectories, providing data-driven insights into protein function and interactions.

Journal of Chemical Information and Modeling · Jan 23, 2025Peer-reviewedBioinformatics