In 2018, the Einstein Foundation and Stiftung Charité awarded a BIH Einstein Visiting Fellowship to a start a new collaboration with Prof. Dr. John Chodera’s lab at the Memorial Sloan Kettering Cancer Center, New York (USA). The resulting research is framed under our OpenKinome umbrella and focuses on developing new approaches that meld structure-informed machine learning with free energy calculations to predict and design kinase polypharmacology.


In this project, we aim to combine structure-enabled machine learning and alchemical free energy calculations to develop a predictive quantitative model to rapidly assess kinase inhibitor affinity and selectivity, design ligands with desired selectivity profiles and assess the impact of clinical point mutations on inhibitor binding.

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In this collaboration with Bayer and the Chodera Lab, we aim to advance and apply KinoML to address pharmaceutically relevant drug design challenges. Special emphasis is put on the effect of point mutations on binding affinity and how these can be exploited to expand the indications of already approved drugs and to guide molecular design.

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