CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction

As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of different chemical substances on different species is a necessary requirement for protecting the environment and ensuring sustainable human development. In this regard, deep learning methods offer significant advantages over traditional experimental approaches in terms of cost, accuracy, and generalization ability. This research introduces CLSSATP, an efficient contrastive self-supervised learning deep neural network prediction model for organic toxicity. The model integrates two modules, a self-supervised learning module using molecular fingerprints for representation, and a contrastive learning module utilizing molecular graphs. Through dual-perspective learning, the model gains clear insights into the structural and property relationships of molecules. The experiment results indicate that our model outperforms comparative methods, demonstrating the effectiveness of our proposed architecture. Moreover, ablation experiments show that the self-supervised module and contrastive learning module respectively provide average performance improvements of 9.43 % and 10.98 % to CLSSATP. Furthermore, by visualizing the representations of our model, we observe that it correctly identifies the substructures that determine the molecular properties, granting itself with interpretability. In conclusion, CLSSATP offers a novel and effective perspective for future research in aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/CLSSATP.