2026​​​​​​​

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• CurvePotGCN: Vishnu and Prathith's work on graph neural networks to predict protein-protein interactions from surface curvature and electrostatic potential published in the Journal of Chemical Sciences: https://doi.org/10.1007/s12039-025-02459-7

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• Hridya's collaborative work published in PNAS: "A pothole-filling strategy for selective targeting of rCUG-repeats associated with myotonic dystrophy type 1": https://doi.org/10.1073/pnas.2507065123

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• Ashish's work on "Spatially Resolved Single-Water Entropy around Amino Acids and Its Link to Hydropathy" published in JPCB:

       https://doi.org/10.1021/acs.jpcb.5c07015

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   2025

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• Our latest publication in Chemical Science: "Water Entropy at the Threonine-rich Surface of Antifreeze and Ice-nucleating Proteins: Small Changes make Big Difference"

       https://doi.org/10.1039/D4SC08383K

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    2024

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• Prathith's  work on "AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations. "  published in JCTC special issue:

       https://doi.org/10.1021/acs.jctc.4c01387

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• Bikirna and Hridya's work on using Memory effects to explain Internal Friction is published in JPCB:

       https://doi.org/10.1021/acs.jpcb.4c05394

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• Amal's work on beta-catenin published in PCCP: Unraveling the folding-assisted unbinding mechanism of TCF with its binding partner ß-catenin.

        https://doi.org/10.1039/D4CP01451K

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• SPOTLIGHT: Sreyas's work on combining Reinforcement Learning with Physics based de novo Drug Design  accepted in Digital Discovery.

       https://doi.org/10.1039/D3DD00194F

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