Changing drug discovery one ratio of partition functions at a time
John Chodera publications
Michael Retchin, Yuanqing Wang, Kenichiro Takaba, and John D. Chodera
[bioRxiv preprint]
We present DrugGym, a sandbox for exploring reinforcement learning strategies and evaluating the economics of decisionmaking strategies and predictive models on small molecule discovery. We use this tool to quantify the value of predictive model accuracy on hit-to-lead programs.
Francesc Sabanés Zariquiey, Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni De Fabritiis
Journal of Chemical Information and Modeling 64:1481, 2024.
[DOI] [preprint]
We show that hybrid neural network / molecular mechanics potentials can significantly improve accuracy over molecular mechanics potentials alone in predicting protein-ligand binding affinities.
David F Hahn, Christopher I Bayly, Hannah E Bruce Macdonald, John D Chodera, Antonia SJS Mey, David L Mobley, Laura Perez Benito, Christina EM Schindler, Gary Tresadern, Gregory L Warren
Preprint ahead of publication: [arXiv] [GitHub]
This living best practices paper for the Living Journal of Computational Molecular Sciences describes the current community consensus in how to curate experimental benchmark data for assessing predictive affinity models for drug discovery, how to prepare these systems for affinity calculations, and how to assess the results to compare performance.
Andrea Rizzi, Travis Jensen, David R. Slochower, Matteo Aldeghi, Vytautas Gapsys, Dimitris Ntekoumes, Stefano Bosisio, Michail Papadourakis, Niel M. Henriksen, Bert L. de Groot, Zoe Cournia, Alex Dickson, Julien Michel, Michael K. Gilson, Michael R. Shirts, David L. Mobley, and John D. Chodera
Journal of Computer Aided Molecular Design 34:601, 2020. [DOI] [PDF] [bioRxiv] [GitHub]
To assess the relative efficiencies of alchemical binding free energy calculations, the SAMPL6 SAMPLing challenge asked participants to submit predictions as a function of computer effort for the same force field and charge model. Surprisingly, we found that most molecular simulation codes cannot agree on the binding free energy was, even for the same force field.
Kai Wang K, John D. Chodera, Yanzhi Yang, and Michael R. Shirts.
J. Comput. Aid. Mol. Des. 27:989, 2013. [DOI] [PDF]
We show how bound ligand poses can be identified even when the location of the binding sites are unknown using the machinery of alchemical modern free energy calculations on graphics processors.
