Benchmarking cross-docking strategies for structure-informed machine learning in kinase drug discovery

Schaller D, Christ CD, Chodera JD, Volkamer A
preprint: [bioRxiv]

We assess strategies for predicting useful docked ligand poses for structure-informed machine learning for kinase inhibitor drug discovery.

Is structure based drug design ready for selectivity optimization?

Steven K. Albanese, John D. Chodera, Andrea Volkamer, Simon Keng, Robert Abel, and Lingle Wang
Journal of Chemical Informatics and Modeling [DOI] [bioRxiv] [GitHub]

We asked whether the similarity of binding sites in related kinases might result in a fortuitous cancellation of errors in using alchemical free energy calculations to predict kinase inhibitor selectivities. Surprisingly, we find that even distantly related kinases have sufficient correlation in their errors that predicting changes in selectivity can be much more accurate than predicting changes in potency due to this effect, and show how this could lead to large reductions in the number of molecules that must be synthesized to achieve a desired selectivity goal.