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.
Authors
- Renjie Zhu (ORCID: https://orcid.org/0000-0002-5888-9506)
- Fei Xia (ORCID: https://orcid.org/0000-0001-9458-9175)
- Tong Zhu (ORCID: https://orcid.org/0000-0001-7472-3736)
- Ruibo Wu (ORCID: https://orcid.org/0000-0002-1984-046X)
Institutions
- 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)
Publication Details
- 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
- Field-Weighted Citation Impact
- 0.00