ArticleJournal of chemical theory and computation2021
Development and Benchmarking of Open Force Field v1.0.0-the Parsley Small-Molecule Force Field.
Article in Journal of chemical theory and computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
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89 citing papers in PubMed.
- SMartini: automated small molecule parametrization for Martini 3 force field.bioRxiv : the preprint server for biology · 2026Article
- Deep residual learning for molecular force fields.Nature communications · 2026Article
- Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.Journal of chemical information and modeling · 2026Article
- Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.Journal of chemical theory and computation · 2026Article
- The Open Molecular Software Foundation (OMSF) and the Growing Role of Open Source Software in Molecular Modeling.Journal of chemical information and modeling · 2026Review
- Automated Force Field Developer and Optimizer Platform: Torsion Reparameterization.Journal of chemical information and modeling · 2026Article
- More Accurate Binding Affinity Prediction Using Protein Homology and Ligand-Based Transfer Learning.Journal of chemical information and modeling · 2026Article
- Advancing NanoLuc Luciferase Stability beyond Directed Evolution and Rational Design through Expert-Guided Deep Learning.ACS catalysis · 2026Article
- Leak Proof PDBBind: A Reorganized Data Set of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.The journal of physical chemistry. B · 2026Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor.Nature communications · 2025Article
- A Simple, Polarizable, Rigid, 3-Point Water Model Using the Direct Polarization Approximation.Journal of chemical theory and computation · 2025Article
- Evaluating the Functional Importance of Conformer-Dependent Atomic Partial Charge Assignment.Journal of computational chemistry · 2025Article
- A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks.Digital discovery · 2025Article
- Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2.Briefings in bioinformatics · 2025Article
- QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.Journal of chemical information and modeling · 2025Article
- Drugit: crowd-sourcing molecular design of non-peptidic VHL binders.Nature communications · 2025Article
- Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.Journal of chemical theory and computation · 2025Article
- Article
- Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage.Chemical science · 2025Article
29 more citing papers are in PubMed but not listed here.
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20 authors.
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Abstract
We present a methodology for defining and optimizing a general force field for classical molecular simulations, and we describe its use to derive the Open Force Field 1.0.0 small-molecule force field, codenamed Parsley. Rather than using traditional atom typing, our approach is built on the SMIRKS-native Open Force Field (SMIRNOFF) parameter assignment formalism, which handles increases in the diversity and specificity of the force field definition without needlessly increasing the complexity of the specification. Parameters are optimized with the ForceBalance tool, based on reference quantum chemical data that include torsion potential energy profiles, optimized gas-phase structures, and vibrational frequencies. These quantum reference data are computed and are maintained with QCArchive, an open-source and freely available distributed computing and database software ecosystem. In this initial application of the method, we present essentially a full optimization of all valence parameters and report tests of the resulting force field against compounds and data types outside the training set. These tests show improvements in optimized geometries and conformational energetics and demonstrate that Parsley's accuracy for liquid properties is similar to that of other general force fields, as is accuracy on binding free energies. We find that this initial Parsley force field affords accuracy similar to that of other general force fields when used to calculate relative binding free energies spanning 199 protein-ligand systems. Additionally, the resulting infrastructure allows us to rapidly optimize an entirely new force field with minimal human intervention.
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