Changing drug discovery one ratio of partition functions at a time
John Chodera publications
Zhang I, Rufa DA, Pulido I, Henry MM, Rosen LE, Hauser K, Singh S, Chodera JD
Journal of Chemical Theory and Computation 19:4863, 2023
We assess what is required for alchemical free energy calculations to be able to make high-quality predictions of the impact of interfacial mutations on protein-protein binding.
Marcus Wieder, Josh Fass, and John Chodera
[bioRxiv] [code] [data]
We show, for the first time, how alchemical free energy calculations can be used to not only compute free energy differences between small molecules involving covalent bond rearrangements in systems treated entirely with quantum machine learning potentials, but that these calculations have the capacity to learn to efficiently generalize from conditioning on experimental free energy data.
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.
Marcus Wieder, Josh Fass, and John D. Chodera
Chemical Science, in press [bioRxiv] [code]
We demonstrate, for the first time, how alchemical free energy calculations can performed on systems simulated entirely with quantum machine learning potentials and how these potentials can be retrained on experimental free energies to generalize to new molecules from limited training data. We apply this approach to a difficult problem in small molecule drug discovery: Predicting accurate tautomer ratios in solution.
Dominic A. Rufa, Hannah E. Bruce Macdonald, Josh Fass, Marcus Wieder, Patrick B. Grinaway, Adrian E. Roitberg, Olexandr Isayev, and John D. Chodera.
Preprint ahead of submission.
[bioRxiv] [GitHub]
In this first use of hybrid machine learning / molecular mechanics (ML/MM) potentials for alchemical free energy calculations, we demonstrate how the improved modeling of intramolecular ligand energetics offered by the quantum machine learning potential ANI-2x can significantly improve the accuracy in predicting kinase inhibitor binding free energy by reducing the error from 0.97~kcal/mol to 0.47~kcal/mol, which could drastically reduce the number of compounds that must be synthesized in lead optimization campaigns for minimal additional computational cost.
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.
Kevin Hauser, Christopher Negron, Steven K. Albanese, Soumya Ray, Thomas Steinbrecher, Robert Abel, John D. Chodera, and Lingle Wang.
Communications Biology 1:70, 2018 [DOI] [PDF] [input files and analysis scripts]
In our first collaborative paper with Schrödinger, we present the first comprehensive benchmark assessing the ability for alchemical free energy calculations to predict clinical mutational resistance or susceptibility to targeted kinase inhibitors using the well-studied kinase Abl, the target of therapy for chronic myelogenous leukemia (CML).
Guilherme Duarte Ramos Matos, Daisy Y. Kyu, Hannes H. Loeffler, John D. Chodera, Michael R. Shirts, David Mobley
Journal of Chemical Engineering Data 62:1559, 2017. [DOI] [bioRxiv] [GitHub]
We review alchemical methods for computing solvation free energies and present an update (version 0.5) to the FreeSolv database of experimental and calculated hydration free energies of neutral compounds.
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.
Anthony Nicholls, David L. Mobley, J. Peter Guthrie, John D. Chodera, and Vijay S. Pande.
J. Med. Chem. 51:769, 2008. [DOI] [PDF]
A blind evaluation of the accuracy of alchemical free energy methods for computing gas-to-water transfer free energies (solvation free energies) of small molecules demonstrates that modern forcefields are likely sufficiently accurate to be useful in drug design.
Michael R. Shirts, David L. Mobley, John D. Chodera, and Vijay S. Pande.
J. Phys. Chem. B 111:13052, 2007. [DOI] [PDF]
We identify a major source of systematic error in absolute alchemical free energy calculations of ligand binding and show how a simple procedure can inexpensively and accurately eliminate it.
