ACtriplet: An improved deep learning model for activity cliffs prediction by in tegrating triplet loss and pre-training.
10.1016/j.jpha.2025.101317
- Author:
Xinxin YU
1
;
Yimeng WANG
1
;
Long CHEN
1
;
Weihua LI
1
;
Yun TANG
1
;
Guixia LIU
1
Author Information
1. Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Publication Type:Journal Article
- Keywords:
Activity cliff;
Deep learning;
Pre-training;
Triplet loss
- From:
Journal of Pharmaceutical Analysis
2025;15(8):101317-101317
- CountryChina
- Language:English
-
Abstract:
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.
