Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

Sukrit Singh, Vytautas Gapsys, Matteo Aldeghi, David Schaller, Aziz M Rangwala, Jessica B White, Joseph P Bluck, Jenke Scheen, William G Glass, Jiaye Guo, Sikander Hayat, Bert L de Groot, Andrea Volkamer, Clara D Christ, Markus A Seeliger, John D Chodera.
[bioRxiv]

We show that alchemical free energy calculations have the potential to prospectively predict the impact of clinical kinase mutations on targeted kinase inhibitor binding.

Nutmeg and SPICE: Models and data for biomolecular machine learning

Peter Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland
Journal of Chemical Theory and Computation 20:8583, 2024.
[DOI] [preprint]

We present a significant expansion of the SPICE dataset, a large-scale quantum chemical dataset for training machine learning potentials, and show how it can be used to build extremely accurate machine learning potentials.

Machine-learned molecular mechanics force fields from large-scale quantum chemical data

Kenichiro Takaba, Anika J Friedman, Chapin E Cavender, Pavan Kumar Behara, Iván Pulido, Michael M Henry, Hugo MacDermott-Opeskin, Christopher R Iacovella, Arnav M Nagle, Alexander Matthew Payne, Michael R Shirts, David L Mobley, John D Chodera, Yuanqing Wang
Chemical Science 15:12861, 2024 [DOI] [arXiv preprint]

We present a new self-consistent MM force field trained on $>$1.1M quantum chemical calculations that uses graph nets to achieve high accuracy and produce accurate protein-ligand binding free energies.

Enhancing protein–ligand binding affinity predictions using neural network potentials

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.