A quantitative structure optimization model based on artificial electric field algorithm for compounds binding to estrogen receptor α

Qingtao Pan; Jun Tang; Jie Yan; Yixiang He; Hao Li; Songyang Lao

doi:10.1117/12.2639885

19 October 2022 A quantitative structure optimization model based on artificial electric field algorithm for compounds binding to estrogen receptor α

Qingtao Pan, Jun Tang, Jie Yan, Yixiang He, Hao Li, Songyang Lao

Author Affiliations +

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945F (2022) https://doi.org/10.1117/12.2639885
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

This paper establishes a quantitative structural optimization model for compounds binding to estrogen receptor α based on the artificial electric field algorithm. The model can effectively screen the high-dimensional molecular descriptors of unknown compounds and realize the quantitative prediction of biological activity and classification prediction of pharmacokinetic properties. In this paper, 1974 candidate compounds used in the treatment of breast cancer were investigated. The results show that the accuracy of quantitative and classification prediction in the model is very high, and it also plays an important role in the virtual structure optimization of compounds binding to estrogen receptor α and can greatly promote the extraction, design, or synthesis of new drugs for the treatment of other diseases from unknown compounds.

Citation Download Citation

Qingtao Pan, Jun Tang, Jie Yan, Yixiang He, Hao Li, and Songyang Lao "A quantitative structure optimization model based on artificial electric field algorithm for compounds binding to estrogen receptor α", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945F (19 October 2022); https://doi.org/10.1117/12.2639885

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