ArticleJournal of chemical theory and computation2018
Escaping Atom Types in Force Fields Using Direct Chemical Perception.
Article in Journal of chemical theory and computation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers.
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Who cites it
95 citing papers in PubMed.
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- Systematic Design of Molecularly Imprinted Polymers for Triclosan Using Design of Experiments and Molecular Dynamics Simulations.Polymers · 2026Article
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- Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.Journal of chemical theory and computation · 2026Article
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- Doing More with Less: Accurate and Scalable Ligand Free Energy Calculations by Focusing on the Binding Site.Journal of chemical information and modeling · 2026Article
- Understanding How Synthetic Impurities Affect Glyphosate Solubility and Crystal Growth Using Free Energy Calculations and Molecular Dynamics Simulations.The journal of physical chemistry. B · 2026Article
- HessFit: Deriving Molecule-Specific Force Fields from Quantum Mechanical Hessians.Methods in molecular biology (Clifton, N.J.) · 2026Article
- In Silico Predictions Driving the Development of 3D-Printed Drug Delivery Systems.Pharmaceutics · 2025Article
- Meeko: Molecule Parametrization and Software Interoperability for Docking and Beyond.Journal of chemical information and modeling · 2025Article
- Synthesis and Persistence Length Study of Defect-Free and Non-Aggregated Conjugated Ladder Polymers.JACS Au · 2025Article
- Condensation of Force Field Parameters from Machine Learning Predicted Distributions for High-Throughput Virtual Screening Applications.Journal of chemical information and modeling · 2025Article
- Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.Macromolecules · 2025Review
- Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.Journal of chemical information and modeling · 2025Article
- Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor.Nature communications · 2025Article
- Beyond Barriers, Big Crystallization Hurdles: Atropisomerism in Beyond Rule of Five Compounds Explored by Computational and NMR Studies.Molecular pharmaceutics · 2025Article
35 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Traditional approaches to specifying a molecular mechanics force field encode all the information needed to assign force field parameters to a given molecule into a discrete set of atom types. This is equivalent to a representation consisting of a molecular graph comprising a set of vertices, which represent atoms labeled by atom type, and unlabeled edges, which represent chemical bonds. Bond stretch, angle bend, and dihedral parameters are then assigned by looking up bonded pairs, triplets, and quartets of atom types in parameter tables to assign valence terms and using the atom types themselves to assign nonbonded parameters. This approach, which we call indirect chemical perception because it operates on the intermediate graph of atom-typed nodes, creates a number of technical problems. For example, atom types must be sufficiently complex to encode all necessary information about the molecular environment, making it difficult to extend force fields encoded this way. Atom typing also results in a proliferation of redundant parameters applied to chemically equivalent classes of valence terms, needlessly increasing force field complexity. Here, we describe a new approach to assigning force field parameters via direct chemical perception. Rather than working through the intermediary of the atom-typed graph, direct chemical perception operates directly on the unmodified chemical graph of the molecule to assign parameters. In particular, parameters are assigned to each type of force field term (e.g., bond stretch, angle bend, torsion, and Lennard-Jones) based on standard chemical substructure queries implemented via the industry-standard SMARTS chemical perception language, using SMIRKS extensions that permit labeling of specific atoms within a chemical pattern. We use this to implement a new force field format, called the SMIRKS Native Open Force Field (SMIRNOFF) format. We demonstrate the power and generality of this approach using examples of specific molecules that pose problems for indirect chemical perception and construct and validate a minimalist yet very general force field, SMIRNOFF99Frosst. We find that a parameter definition file only ∼300 lines long provides coverage of all but <0.02% of a 5 million molecule drug-like test set. Despite its simplicity, the accuracy of SMIRNOFF99Frosst for small molecule hydration free energies and selected properties of pure organic liquids is similar to that of the General Amber Force Field, whose specification requires thousands of parameters. This force field provides a starting point for further optimization and refitting work to follow.
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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.