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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
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Eastman P, Galvelis R, Peláez RP, Abreu CRA, Farr SE, Gallicchio E, Gorenko A, Henry MH, Hu F, Huang J, Krämer A, Michel J, Mitchell J, Pande VS, Rodrigues JPGLM, Rodriguez-Guerra J, Simmonett AC, Swails J, Turner P, Wang Y, Zhang I, Chodera JD, De Fabritiis G, Markland TE
Journal of Physical Chemistry B [DOI] [website] [code]
We present OpenMM 8, which includes GPU-accelerated support for simulating hybrid ML/MM systems that use machine learning (ML) potentials to achieve high accuracy with minimal loss in speed.
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
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Takaba K, Pulido I, Behara PK, Henry M, MacDermott-Opeskin H, Chodera JD, Wang Y
preprint: [arXiv]
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.
Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors
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Boby ML, Fearon D, Ferla M, Filep M, Robinson MC, The COVID Moonshot Consortium, Chodera JD, Lee A, London N, von Delft F.
Science 382:eabo7201, 2023 [DOI] [PDF] [ready to use data]
We report the discovery of a new oral antiviral non-covalent SARS-CoV-2 main protease inhibitor developed by the COVID Moonshot, a global open science collaboration leveraging free energy calculations on Folding@home and ML-accelerated synthesis planning, now in accelerated preclinical studies funded by an $11M grant from the WHO ACT-A program via the Wellcome Trust. We are currently in discussions with generics manufacturers about partnering with us throughout clinical trials to ensure we can scale up production for global equitable and affordable access once approved by regulatory agencies.
Benchmarking cross-docking strategies for structure-informed machine learning in kinase drug discovery
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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.
NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics
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Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland TE, Chodera JD, and de Fabritiis G
Journal of Chemical Information and Modeling 63:5701, 2023 [DOI] [arXiv]
We demonstrate that a new generation of quantum machine learning (QML) potentials based on neural networks---which can achieve quantum chemical accuracy at a fraction of the cost---can be implemented efficiently in the OpenMM molecular dynamics simulation engine as part of hybrid machine learning / molecular mechanics (ML/MM) potentials that promise to deliver superior accuracy for modeling protein-ligand interactions.
Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex
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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.
Development and benchmarking of Open Force Field 2.0.0---the Sage small molecule force field
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Boothroyd S, Behara PK, Madin OC, Hahn DF, Jang H, Gapsys V, Wagner JR, Horton JT, Dotson DL, Thompson MW, Maat J, Gokey T, Wang L-P, Cole DJ, Gilson MK, Chodera JD, Bayly CI, Shirts MR, Mobley DL
Journal of Chemical Theory and Computation 19:3251, 2023 [DOI] [chemRxiv] [GitHub] [examples]
We present a new generation of small molecule force field for molecular design from the Open Force Field Initiative fit to both quantum chemical and experimental liquid mixture data
MEN1 mutations mediate clinical resistance to menin inhibition
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Perner F, Stein EM, Wenge DV, Singh S, Kim J, Apazidis A, Rahnamoun H, Anand D, Marinaccio C, Hatton C, Wen Y, Stone RM, Schaller D, Mowla S, Xiao W, Gamlen HA, Stonestrom AJ, Persaud S, Ener E, Cutler JA, Doench JG, McGeehan GM, Volkamer A, Chodera JD, Nowak RP, Fischer ES, Levine RL, Armstrong SA, Cai SF
Nature 615:913, 2023 [DOI]
We describe how mutants that confer therapeutic resistance to menin inhibition impact small molecule binding but not interactions with the natural ligand MLL1.
Turning high-throughput structural biology into predictive inhibitor design
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Saar KL, McCorkindale W, Fearon D, Boby M, Barr H, Ben-Shmuel A, COVID Moonshot Consortium, London N, von Delft F, Chodera JD, Lee AA
PNAS 120:e2214168120, 2023 [DOI]
We demonstrate how potent inhibitors can be predicted from high-throughput structural biology, demonstrating this approach against the SARS-CoV-2 main viral protease (Mpro).
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
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Wang Y, Pulido I, Takaba K, Kaminow B, Scheen J, Wang L, Chodera JD
preprint: [arXiv]
We present a drop-in replacement for generating AM1-BCC ELF10 charges based on graph convolutional nets that is orders of magnitude faster than standard methods for both small molecules and biomolecules.
Spatial attention kinetic network with E(n) equivariance
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Yuanqing Wang and John D. Chodera
preprint: [arXiv] [code]
This work descibes Spatial Attention Kinetic Networks (SAKE), a new E(n)-equivariant architecture that uses spatial attention, enabling the construction of extremely performant but still accurate machine learning potentials, as well as flows capable of prediction dynamics.
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
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Eastman P, Behara PK, Dotson DL, Galvelis R, Herr JE, Horton JT, Mao Y, Chodera JD, Pritchard BP, Wang Y, De Fabritiis G, and Markland TE
Scientific Data 10:11, 2023 [DOI]
To remedy the lack of large, open quantum chemical datasets for training accurate general machine learning potentials and molecular mechanics force fields for druglike small molecules and biomolecules, we produce the open SPICE dataset, and show how it can be used to build extremely accurate machine learning potentials.
Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale
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Horton JT, Boothroyd S, Wagner W, Mitchell JA, Gokey T, Dotson DL, Behara PK, Ramaswamy VK, Mackey M, Chodera JD, Anwar J, Mobley DL, and Cole DJ
Journal of Chemical Informatics and Modeling 62:22, 2022 [DOI]
We describe an automated pipeline for generating tailored force field parameters for small molecules using quantum chemical or quantum machine learning potentials.
End-to-end differentiable molecular mechanics force field construction
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Yuanqing Wang, Josh Fass, and John D. Chodera
Chemical Science 13:12016, 2022 [DOI] [arXiv] [pytorch code] [JAX code]
Molecular mechanics force fields have been a workhorse for computational chemistry and drug discovery. Here, we propose a new approach to force field parameterization in which graph convolutional networks are used to perceive chemical environments and assign molecular mechanics (MM) force field parameters. The entire process of chemical perception and parameter assignment is differentiable end-to-end with respect to model parameters, allowing new force fields to be easily constructed from MM or QM force fields, extended, and applied to arbitrary biomolecules.
Improving force field accuracy by training against condensed-phase mixture properties
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Boothroyd S, Madin OC, Mobley DL, Wang L-P, Chodera JD, and Shirts MR
Journal of Chemical Theory and Computation 18:3577, 2022 [DOI] [GitHub]
We use a new automated framework for physical property evaluation and fitting to show how molecular mechanics force fields can be systematically improved by fitting to condensed phase properties.
SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction
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Grosjean H, Isik M, Aimon A, Mobley D, Chodera JD, von Delft F, and Biggin PC
Journal of Computer-Aided Molecular Design 36:291, 2022 [DOI]
We field a blind community challenge to assess how well state of the art computational chemistry methods can predict the binding modes of small druglike fragments to a protein target for which no chemical matter is known, PHIP2, using fragment screening at the Diamond Light Source.
CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding
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Ackloo S, Al-awar R, Amaro RE, Arrowsmith CH, Azevedo H, Batey RA, Bengio Y, Betz UAK, Bologa CG, Chodera JD, Cornell WD, Dunham I, Ecker GF, Edfeldt K, Edwards AM, Gilsom MK, Gordijo CR, Hessler G, Hillisch A, Hogner A, Irwin JJ, Jansen JM, Kuhn D, Leach AR, Lee AA, Lessel U, Moult J, Muegge I, Oprea TI, Perry BG, Riley, Singh Saikantendu K, Santhakumar V, Schapira M, Scholten C, Todd MH, Vedadi M, Volkamer A, and Wilson TM
Nature Reviews Chemistry 6:287, 2022 [DOI]
We describe CACHE: A new public-private partnership that aims to transform computer-aided drug discovery much the way that CASP transformed protein structure prediction into a reproducible, accurate engineering discipline.
INK4 tumor suppressor proteins mediate resistance to CDK4/6 kinase inhibitors
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Li Q, Jiang B, Guo J, Shao H, Del Priore IS, Chang Q, Kudo R, Li Z, Razavi P, Liu B, Boghossian AS, Rees MG, Ronan MM, Roth JA, Donovan KA, Palafox M, Reis-Filho JS, de Stanchina E, Fischer ES, Rosen N, Serra V, Koff A, Chodera JD, Gray NS, and Chandardlapaty S
Cancer Discovery} 12:356, 2022 [DOI]
We demonstrate CDK6 causes drug resistance by binding INK4 proteins, and develop bifunctional degraders conjugating palbociclib with E3 ligands to overcome this mechanism of resistance.
Capturing non-local through-bond effects in molecular mechanics force fields: II. Using fractional bond orders to fit torsion parameters
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Stern CD, Maat J, Dotson DL, Bayly CI, Smith DGA, Mobley DL, and Chodera JD
preprint: [bioRxiv]
We show how the Wiberg Bond Order (WBO) can be used to accurately interpolate torsional profiles for molecular mechanics force fields, which holts the potential for drastically reducing the complexity of these force fields while increasing their ability to generalize and accurately treat complex druglike molecules such as kinase inhibitors.