ArticleJournal of chemical theory and computation2018
Optimized Lennard-Jones Parameters for Druglike Small Molecules.
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 29 papers.
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Who cites it
29 citing papers in PubMed.
- Training a force field for proteins and small molecules from scratch.Chemical science · 2026Article
- Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.Journal of chemical theory and computation · 2026Article
- Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.Journal of chemical theory and computation · 2025Article
- ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation.Journal of chemical theory and computation · 2025Article
- easyPARM: Automated, Versatile, and Reliable Force Field Parameters for Metal-Containing Molecules with Unique Labeling of Coordinating Atoms.Journal of chemical theory and computation · 2025Article
- CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.The journal of physical chemistry. B · 2024Review
- Machine-learned molecular mechanics force fields from large-scale quantum chemical data.Chemical science · 2024Article
- Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.The journal of physical chemistry. B · 2024Article
- Calculations of Absolute Solvation Free Energies with Transformato─Application to the FreeSolv Database Using the CGenFF Force Field.Journal of chemical theory and computation · 2023Article
- OFraMP: a fragment-based tool to facilitate the parametrization of large molecules.Journal of computer-aided molecular design · 2023Article
- Replica-Exchange Enveloping Distribution Sampling Using Generalized AMBER Force-Field Topologies: Application to Relative Hydration Free-Energy Calculations for Large Sets of Molecules.Journal of chemical information and modeling · 2022Article
- Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation.Journal of chemical theory and computation · 2022Article
- Quantitative molecular simulations.Physical chemistry chemical physics : PCCP · 2022Review
- Harnessing Deep Learning for Optimization of Lennard-Jones Parameters for the Polarizable Classical Drude Oscillator Force Field.Journal of chemical theory and computation · 2022Article
- Bayesian-Inference-Driven Model Parametrization and Model Selection for 2CLJQ Fluid Models.Journal of chemical information and modeling · 2022Article
- Computational methods and theory for ion channel research.Advances in physics: X · 2022Article
- Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules.Frontiers in molecular biosciences · 2022Article
- Global Optimization of the Lennard-Jones Parameters for the Drude Polarizable Force Field.Journal of chemical theory and computation · 2021Article
- Systematic optimization of a fragment-based force field against experimental pure-liquid properties considering large compound families: application to oxygen and nitrogen compounds.Physical chemistry chemical physics : PCCP · 2021Article
- Evaluation of nine condensed-phase force fields of the GROMOS, CHARMM, OPLS, AMBER, and OpenFF families against experimental cross-solvation free energies.Physical chemistry chemical physics : PCCP · 2021Article
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5 authors.
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Abstract
Meaningful efforts in computer-aided drug design (CADD) require accurate molecular mechanical force fields to quantitatively characterize protein-ligand interactions, ligand hydration free energies, and other ligand physical properties. Atomic models of new compounds are commonly generated by analogy from the predefined tabulated parameters of a given force field. Two widely used approaches following this strategy are the General Amber Force Field (GAFF) and the CHARMM General Force Field (CGenFF). An important limitation of using pretabulated parameter values is that they may be inadequate in the context of a specific molecule. To resolve this issue, we previously introduced the General Automated Atomic Model Parameterization (GAAMP) for automatically generating the parameters of atomic models of small molecules, using the results from ab initio quantum mechanical (QM) calculations as target data. The GAAMP protocol uses QM data to optimize the bond, valence angle, and dihedral angle internal parameters, and atomic partial charges. However, since the treatment of van der Waals interactions based on QM is challenging and may often be unreliable, the Lennard-Jones 6-12 parameters are kept unchanged from the initial atom types assignments (GAFF or CGenFF), which limits the accuracy that can be achieved by these models. To address this issue, a new set of Lennard-Jones 6-12 parameters was systematically optimized to reproduce experimental neat liquid densities and enthalpies of vaporization for a large set of 430 compounds, covering a wide range of chemical functionalities. Calculations of the hydration free energy indicate that optimal accuracy for these models is achieved when the molecule-water van der Waals dispersion is rescaled by a factor of 1.115. The final optimized model yields an average unsigned error of 0.79 kcal/mol in the hydration free energies.
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