TY - JOUR

T1 - Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential

AU - Tkaczyk, Sara

AU - Karwounopoulos, Johannes

AU - Schöller, Andreas

AU - Woodcock, H. Lee

AU - Langer, Thierry

AU - Boresch, Stefan

AU - Wieder, Marcus

N1 - Accession Number
WOS:001191238200001
PubMed ID
38527958

PY - 2024/4/9

Y1 - 2024/4/9

N2 - To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system’s potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959 .] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

AB - To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system’s potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959 .] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

UR - http://www.scopus.com/inward/record.url?scp=85188966863&partnerID=8YFLogxK

U2 - 10.1021/acs.jctc.3c01274

DO - 10.1021/acs.jctc.3c01274

M3 - Article

C2 - 38527958

AN - SCOPUS:85188966863

VL - 20

SP - 2719

EP - 2728

JO - Journal of Chemical Theory and Computation

JF - Journal of Chemical Theory and Computation

SN - 1549-9618

IS - 7

ER -