ArticleJournal of chemical theory and computation2021
Global Optimization of the Lennard-Jones Parameters for the Drude Polarizable 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 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Optimization of Lennard-Jones Parameters for Induced Dipole Polarizable Gaussian Multipole Force Field.The journal of physical chemistry. B · 2025Article
- Non-Covalent Molecular Interaction Rules to Define Internal Dimer Coordinates for Quantum Mechanical Potential Energy Scans.Journal of computational chemistry · 2025Article
- Increasing the Accuracy and Robustness of the CHARMM General Force Field with an Expanded Training Set.Journal of chemical theory and computation · 2025Article
- CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.The journal of physical chemistry. B · 2024Review
- Monte Carlo simulations for free energies of hydration: Past to present.The Journal of chemical physics · 2024Article
- Transferability of Buckingham Parameters for Short-Range Repulsion between Topological Atoms.The journal of physical chemistry. A · 2024Article
- Enthalpies and entropies of hydration from Monte Carlo simulations.Physical chemistry chemical physics : PCCP · 2024Article
- Simulating the Fluorescence of the Locally Excited State of DMABN in Solvents of Different Polarities.The journal of physical chemistry. B · 2024Article
- Simulating the Fluorescence of Di-8-ANEPPS in Solvents of Different Polarity.The journal of physical chemistry. B · 2024Article
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- 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
- Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules.Frontiers in molecular biosciences · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Molecular dynamics (MD) simulations based on atomic models play an important role in the drug-discovery process to screen molecules, estimate binding free energies, and optimize lead compounds in chemical space. Accurate computations of thermodynamic and kinetic properties using MD simulations are highly dependent on the accuracy of the underlying atomic force field. In this context, going beyond the nonpolarizable fixed-charge model by accounting explicitly for induced polarization is highly desirable. The CHARMM polarizable force field based on classical Drude oscillators, in which an auxiliary charged particle is attached via a harmonic spring to its parent nucleus, offers both a computationally convenient and rigorous framework to model explicitly induced electronic polarization in MD simulations. For any molecule of interest, electrostatic partial charges, atomic polarizabilities, and Thole shielding factors, as well as bonded parameters can either be determined from ab initio calculations or ascribed from the knowledge-based library of the CHARMM Generalized force field. While this approach is fairly reliable in general, it is well understood that the overall accuracy of the models with respect to thermodynamic properties such as bulk density, enthalpies, and solvation free energies is particularly sensitive to the nonbonded Lennard-Jones (LJ) parameters. In the present study, we systematically refined the set of LJ parameters for the atom types available in the Drude force field to best match the experimental thermodynamic properties for 416 small druglike organic molecules. To further test the transferability of the optimized parameters, the hydration free energy of 372 molecules was computed. The calculations resulted in a small average error of 0.46 kcal/mol and a Pearson
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