Multi-task aquatic toxicity prediction model based on multi-level features fusion

Abstract

Introduction: With the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability.

Objectives: This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity.

Methods: The model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi.

Results: The experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi.

Conclusion: In summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.

Keywords: Acute toxicity; Deep learning; Molecular fingerprints; Molecular graph features; Multi-task model.

Graphical abstract

Fig. 1

(A) The amount of data for each fish species and its proportion to the total population. (B) The number of non-toxic and toxic compounds on each fish species.

Fig. 2

(A) The overview of ATFPGT-multi. (B) The architecture of the molecular graph representation module, which presents the graph-based representation approach. (C) Shows the transformer layer details based on the multi-head attention mechanism. (D) Describes the process of focused dot product attention layer.

Fig. 3

Comparative analysis of ATFPGT-multi and ATFPGT-single for four fish species datasets.

Fig. 4

The performance of ATFPGT-multi and the comparative algorithms for each fish species datasets.

Fig. 5

Comparison analysis between ATFPGT-multi and its six variant models on four fish datasets.

Fig. 6

Visualization of atomic attention weights. (A) The exemplar molecule selected from BS dataset is presented. (B) The exemplar molecule chosen from RT dataset is displayed. (C) The exemplar molecule selected from FHM dataset is presented. (D) The exemplar molecule selected from SHM dataset is presented.