ArticleChemical science2023
Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.Journal of chemical theory and computation · 2025Article
- Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials.Journal of chemical theory and computation · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- Improved Description of Environment and Vibronic Effects with Electrostatically Embedded ML Potentials.The journal of physical chemistry letters · 2025Article
- Incorporating Neural Networks into the AMOEBA Polarizable Force Field.The journal of physical chemistry. B · 2024Article
- From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.Journal of chemical information and modeling · 2024Article
- Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects.Chemical science · 2023Article
- Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions.Journal of the American Chemical Society · 2023Article
- The development of nucleic acids force fields: From an unchallenged past to a competitive future.Biophysical journal · 2023Review
- Machine Learning Interatomic Potentials and Long-Range Physics.The journal of physical chemistry. A · 2023Review
- Using machine learning to predict the effects and consequences of mutations in proteins.Current opinion in structural biology · 2023Review
- Modeling pyranose ring pucker in carbohydrates using machine learning and semi-empirical quantum chemical methods.Journal of computational chemistry · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore the introduction of the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.