Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Greener, Joe G; Jones, David T.

doi:10.1101/2021.02.05.429941

Cited by 2 publications

(2 citation statements)

References 65 publications

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“…[7,41]). While this approach is not new, recent advances and increase interest in the field of QML potentials have motivated new developments in this field (e.g., [42][43][44]).…”

Section: Qml Potentials Can Be Trained Using Experimental Thermodynamic Data To Systematically Improve Accuracymentioning

confidence: 99%

Teaching free energy calculations to learn from experimental data

Wieder

Fass

Chodera

2021

Preprint

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Alchemical free energy calculations are an important tool in the computational chemistry tool-box, enabling the efficient calculation of quantities critical for drug discovery such as ligand binding affinities, selectivities, and partition coefficients. However, modern alchemical free energy calculations suffer from three significant limitations: (1) modern molecular mechanics force fields are limited in their ability to model complex molecular interactions, (2) classical force fields are unable to treat phenomena that involve rearrangements of chemical bonds, and (3) these calculations are unable to easily learn to improve their performance if readily-available experimental data is available. Here, we show how all three limitations can be overcome through the use of quantum machine learning (QML) potentials capable of accurately modeling quantum chemical energetics even when chemical bonds are made and broken. Because these potentials are based on mathematically convenient deep learning architectures instead of traditional quantum chemical formulations, QML simulations can be run at a fraction of the cost of quantum chemical simulations using modern graphics processing units (GPUs) and machine learning frameworks. We demonstrate that alchemical free energy calculations in explicit solvent are especially simple to implement using QML potentials because these potentials lack singularities and other pathologies typical of molecular mechanics potentials, and that alchemical free energy calculations are highly effective even when bonds are broken or made. Finally, we show how a limited number of experimental free energy measurements can be used to significantly improve the accuracy of computed free energies for unrelated compounds with no significant generalization gap. We illustrate these concepts on the prediction of aqueous tautomer free energies (related to tautomer ratios), which are highly relevant to drug discovery in that more than a quarter of all approved drugs exist as a mixture of tautomers.

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“…[7,41]). While this approach is not new, recent advances and increase interest in the field of QML potentials have motivated new developments in this field (e.g., [42][43][44]).…”

Section: Qml Potentials Can Be Trained Using Experimental Thermodynamic Data To Systematically Improve Accuracymentioning

confidence: 99%

Teaching free energy calculations to learn from experimental data

Wieder

Fass

Chodera

2021

Preprint

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“…The two key points of TorchMD are that, being written in PyTorch, it is very easy to integrate other ML PyTorch models, like ab initio neural network potentials (NNPs) 5 , 22 and machine learning coarse-grained potentials. 8 , 9 Second, TorchMD provides the capability to perform end-to-end differentiable simulations, 14 , 23 , 24 being differentiable on all of its parameters. Similarly, Jax 25 was used to perform end-to-end differentiable molecular simulations on Lennard-Jones systems 26 and for biomolecular systems as well.…”

Section: Introductionmentioning

confidence: 99%

TorchMD: A Deep Learning Framework for Molecular Simulations

Doerr

Majewski

Pérez

et al. 2021

J. Chem. Theory Comput.

152

121

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Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at .

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Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Cited by 2 publications

References 65 publications

Teaching free energy calculations to learn from experimental data

Teaching free energy calculations to learn from experimental data

TorchMD: A Deep Learning Framework for Molecular Simulations

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