A Coarse-Grained Model for Small Organic Molecules with Optimized Lennard-Jones Parameters

Abstract Developing accurate coarse-grained (CG) molecular models is crucial for constructing databases of complex chemical molecules and enabling data-driven research. Here we present the OLJCG model for small organic molecules, with Lennard-Jones (LJ) parameters optimized via the Lennard-Jones Static Potential Matching method based on the GAFF2 force field. The OLJCG model is systematically evaluated on two datasets (DS58 and DS29) for densities, vaporization enthalpies, nonaqueous solvation free energies, hydration free energies, and transfer free energies. The results demonstrate that the optimized LJ parameters effectively preserve the description of nonbonded interactions from the all-atom force field. The primary sources of error are identified as the neglect of electrostatic interactions and the use of a nonoptimized water model, providing clear directions for future refinement. The optimization strategy proposed here provides a systematic route for developing CG small-molecule databases.

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Journal
The Journal of Physical Chemistry B
Published
2026-09-15
DOI
https://doi.org/10.1021/acs.jpcb.6c05069
Primary Topic
Machine Learning in Materials Science
Type
article
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article

A Coarse-Grained Model for Small Organic Molecules with Optimized Lennard-Jones Parameters

Renjie Zhu, Fei Xia, Tong Zhu, Ruibo Wu
The Journal of Physical Chemistry B
Machine Learning in Materials Science
article

A Coarse-Grained Model for Small Organic Molecules with Optimized Lennard-Jones Parameters

Renjie Zhu, Fei Xia, Tong Zhu, Ruibo Wu
article en

Abstract

Abstract Developing accurate coarse-grained (CG) molecular models is crucial for constructing databases of complex chemical molecules and enabling data-driven research. Here we present the OLJCG model for small organic molecules, with Lennard-Jones (LJ) parameters optimized via the Lennard-Jones Static Potential Matching method based on the GAFF2 force field. The OLJCG model is systematically evaluated on two datasets (DS58 and DS29) for densities, vaporization enthalpies, nonaqueous solvation free energies, hydration free energies, and transfer free energies. The results demonstrate that the optimized LJ parameters effectively preserve the description of nonbonded interactions from the all-atom force field. The primary sources of error are identified as the neglect of electrostatic interactions and the use of a nonoptimized water model, providing clear directions for future refinement. The optimization strategy proposed here provides a systematic route for developing CG small-molecule databases.

The Journal of Physical Chemistry B
National Sun Yat-sen University (TW), Sun Yat-sen University (CN), New York University Shanghai (CN), Sun Yat-sen Memorial Hospital (CN), East China Normal University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 24%
Machine Learning in Materials Science
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A Coarse-Grained Model for Small Organic Molecules with Optimized Lennard-Jones Parameters — Renjie Zhu, Fei Xia, et al. · The Journal of Physical Chemistry B (2026) | TGRS Research Map | TGRS