Intelligent Algorithm-guided Parameter Learning for the ABEEM Model

doi:10.1007/s40242-025-5153-2

Chemical Research in Chinese Universities ›› 2025, Vol. 41 ›› Issue (5): 1114-1120.doi: 10.1007/s40242-025-5153-2

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Intelligent Algorithm-guided Parameter Learning for the ABEEM Model

MENG Peiran¹, YOU Zhuo¹, GUO Kaixuan¹, YU Chunyang², GONG Lidong¹, YANG Zhongzhi¹

1. School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, P. R. China;
2. State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China

Received:2025-07-21 Accepted:2025-08-28 Online:2025-10-01 Published:2025-09-26
Contact: GONG Lidong, E-mail: gongjw@lnnu.edu.cn E-mail:gongjw@lnnu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (No. 22377048).

Abstract

Abstract: Accurate modeling of charge distribution plays a vital role in molecular simulations, electrostatic energy evaluation, and mechanistic analysis. The atom-bond electronegativity equalization method (ABEEM) provides a physically interpretable framework for computing atomic and electronic site charges by partitioning molecular space into atoms, bonds, and lone-pair regions. However, conventional ABEEM parameterization relies heavily on manual tuning, limiting its adaptability and predictive accuracy. In this work, an automated parameter learning strategy for ABEEM was proposed, guided by intelligent optimization algorithms and formulated within a goal programming framework. The framework systematically calibrates the key parameters of multiple types of charge sites. A chemically diverse training set including proteins, lipids, and nucleotides was constructed, and a dual-level objective function was designed to improve accuracy at both site and atomic levels. This approach significantly enhances the predictive performance and consistency of the ABEEM model across complex biomolecular systems. It also eliminates human bias and provides a scalable and generalizable pathway for force field development.

Key words: Charge distribution, Atom-bond electronegativity equalization method (ABEEM), Intelligent optimization

MENG Peiran, YOU Zhuo, GUO Kaixuan, YU Chunyang, GONG Lidong, YANG Zhongzhi. Intelligent Algorithm-guided Parameter Learning for the ABEEM Model[J]. Chemical Research in Chinese Universities, 2025, 41(5): 1114-1120.

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Intelligent Algorithm-guided Parameter Learning for the ABEEM Model

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