ArticleChemical science2020
Simulating protein-ligand binding with neural network potentials.
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Enabling Biomolecular Simulations with Neural Network Potentials in GROMACS.Journal of chemical theory and computation · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- 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
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and machine learning potential.Chemical science · 2026Article
- CHARMM-GUI Hybrid ML/MM Builder for Hybrid Machine Learning and Molecular Mechanical Modeling and Simulations.Journal of chemical information and modeling · 2026Article
- A conformational benchmark for optical property prediction with solvent-aware graph neural networks.Communications chemistry · 2026Article
- A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics.Nature communications · 2025Article
- Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.Journal of the American Chemical Society · 2025Article
- Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials.Chemical science · 2024Article
- Incorporating Neural Networks into the AMOEBA Polarizable Force Field.The journal of physical chemistry. B · 2024Article
- Implementation and Validation of an OpenMM Plugin for the Deep Potential Representation of Potential Energy.International journal of molecular sciences · 2024Article
- NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics.Journal of chemical information and modeling · 2023Article
- Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations.Journal of chemical information and modeling · 2023Review
- Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects.Chemical science · 2023Article
- Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins.Journal of chemical information and modeling · 2023Article
- SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.Scientific data · 2023Article
- In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses.Infection and drug resistance · 2023Review
- Modeling pyranose ring pucker in carbohydrates using machine learning and semi-empirical quantum chemical methods.Journal of computational chemistry · 2022Article
- Application of Computational Biology and Artificial Intelligence in Drug Design.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug molecules adopt a range of conformations both in solution and in their protein-bound state. The strain and reduced flexibility of bound drugs can partially counter the intermolecular interactions that drive protein-ligand binding. To make accurate computational predictions of drug binding affinities, computational chemists have attempted to develop efficient empirical models of these interactions, although these methods are not always reliable. Machine learning has allowed the development of highly-accurate neural-network potentials (NNPs), which are capable of predicting the stability of molecular conformations with accuracy comparable to state-of-the-art quantum chemical calculations but at a billionth of the computational cost. Here, we demonstrate that these methods can be used to represent the intramolecular forces of protein-bound drugs within molecular dynamics simulations. These simulations are shown to be capable of predicting the protein-ligand binding pose and conformational component of the absolute Gibbs energy of binding for a set of drug molecules. Notably, the conformational energy for anti-cancer drug erlotinib binding to its target was found to be considerably overestimated by a molecular mechanical model, while the NNP predicts a more moderate value. Although the ANI-1ccX NNP was not trained to describe ionic molecules, reasonable binding poses are predicted for charged ligands, but this method is not suitable for modeling charged ligands in solution.
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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.