FeNNol: An efficient and flexible library for building force-field-enhanced neural network potentials

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(4)

Published: July 25, 2024

Neural network interatomic potentials (NNPs) have recently proven to be powerful tools accurately model complex molecular systems while bypassing the high numerical cost of ab initio dynamics simulations. In recent years, numerous advances in architectures as well development hybrid models combining machine-learning (ML) with more traditional, physically motivated, force-field interactions considerably increased design space ML potentials. this paper, we present FeNNol, a new library for building, training, and running force-field-enhanced neural It provides flexible modular system building models, allowing us easily combine state-of-the-art embeddings ML-parameterized physical interaction terms without need explicit programming. Furthermore, FeNNol leverages automatic differentiation just-in-time compilation features Jax Python enable fast evaluation NNPs, shrinking performance gap between standard force-fields. This is demonstrated popular ANI-2x reaching simulation speeds nearly on par AMOEBA polarizable commodity GPUs (graphics processing units). We hope that will facilitate application NNP wide range problems.

Language: Английский

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Validating Structural Predictions of Conjugated Macromolecules in Espaloma-Enabled Reproducible Workflows

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(2), P. 478 - 478

Published: Jan. 8, 2025

We incorporated Espaloma forcefield parameterization into MoSDeF tools for performing molecular dynamics simulations of organic molecules with HOOMD-Blue. compared equilibrium morphologies predicted perylene and poly-3-hexylthiophene (P3HT) the ESP-UA in present work against prior using OPLS-UA forcefield. found that, after resolving chemical ambiguities topologies, is similar to GAFF. observed clustering/melting phase behavior be between OPLS-UA, but base energy unit was better connect experimentally measured transition temperatures. Short-range ordering by radial distribution functions essentially identical two forcefields, long-range grazing incidence X-ray scattering qualitatively similar, matching experiments than OPLS-UA. concluded that offers promise automated screening are from more complex spaces.

Language: Английский

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Fine-tuning molecular mechanics force fields to experimental free energy measurements

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via calculations that can estimate quantities relevant drug discovery such as affinities, selectivities, the impact target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types parameters, modern approaches based graph neural networks (GNNs) learn continuous embedding vectors represent chemical environments from which parameters be generated. Excitingly, GNN parameterization provide a fully end-to-end differentiable model offers possibility systematically improving these models experimental data. In this study, we treat pretrained field-here, espaloma-0.3.2-as foundation simulation fine-tune its charge limited hydration data, with goal assessing degree to improve prediction other related energies. We demonstrate highly efficient "one-shot fine-tuning" method an exponential (Zwanzig) reweighting estimator accuracy without need resimulate configurations. To achieve "one-shot" improvement, importance effective sample size (ESS) regularization strategies retain good overlap between initial fine-tuned fields. Moreover, show leveraging low-rank projections comparable improvements higher-dimensional in variety data-size regimes. Our results linearly-perturbative fine-tuning electrostatic data cost-effective strategy achieves state-of-the-art performance energies FreeSolv dataset.

Language: Английский

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Molecular dynamics and machine learning unlock possibilities in beauty design—A perspective

AIP Advances, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 1, 2025

Computational molecular design—the endeavor to design molecules, with various missions, aided by machine learning and dynamics approaches—has been widely applied create valuable new entities, from small molecule therapeutics protein biologics. In the data regime, physics-based approaches model interaction between being designed proteins of key physiological functions, providing structural insights into mechanism. When abundant have collected, a quantitative structure–activity relationship can be more directly constructed experimental data, which distill guide next round experiment design. Machine methodologies also facilitate physical modeling, improving accuracy force fields extending them unseen chemical spaces enhancing sampling on conformational spaces. We argue that these techniques are mature enough not just extend longevity life but beauty it manifests. this Perspective, we review current frontiers in research development skincare products, as well statistical toolbox applicable addressing challenges industry. Feasible interdisciplinary projects proposed harness power tools innovative, effective, inexpensive products.

Language: Английский

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ABFML: A problem-oriented package for rapidly creating, screening, and optimizing new machine learning force fields

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(5)

Published: Feb. 4, 2025

Machine Learning Force Fields (MLFFs) require ongoing improvement and innovation to effectively address challenges across various domains. Developing MLFF models typically involves extensive screening, tuning, iterative testing. However, existing packages based on a single mature descriptor or model are unsuitable for this process. Therefore, we developed package named ABFML, PyTorch, which aims promote by providing developers with rapid, efficient, user-friendly tool constructing, validating new force field models. Moreover, leveraging standardized module operations cutting-edge machine learning frameworks, can swiftly establish In addition, the platform seamlessly transition graphics processing unit environments, enabling accelerated calculations large-scale parallel simulations of molecular dynamics. contrast traditional from-scratch approaches development, ABFML significantly lowers barriers developing models, thereby expediting application within development

Language: Английский

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