文献类型：期刊文献

英文题名：ACtriplet: An improved deep learning model for activity cliffs prediction by in tegrating triplet loss and pre-training

作者：Yu, Xinxin[1];Wang, Yimeng[1];Chen, Long[1];Li, Weihua[1];Tang, Yun[1];Liu, Guixia[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2025

卷号：15

期号：8

外文期刊名：JOURNAL OF PHARMACEUTICAL ANALYSIS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001595266900002)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant Nos.: U23A20530, 82273858, and 82173746) , the National Key Research and Development Program of China (Grant No.: 2023YFF1204 904) , and Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission, China) .

语种：英文

外文关键词：Activity cliff; Triplet loss; Deep learning; Pre-training

摘要：Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures. Nonetheless, they also form a major source of prediction error in structure-activity relationship (SAR) models. To date, several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs. In this paper, we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet, tailored for ACs. Through extensive comparison with multiple baseline models on 30 benchmark datasets, the results showed that ACtriplet was significantly better than those deep learning (DL) models without pre-training. In addition, we explored the effect of pre-training on data representation. Finally, the case study demonstrated that our model's interpretability module could explain the prediction results reasonably. In the dilemma that the amount of data could not be increased rapidly, this innovative framework would better make use of the existing data, which would propel the potential of DL in the early stage of drug discovery and optimization. (c) 2025 The Authors. Published by Elsevier B.V. on behalf of Xi'an Jiaotong University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).