Deep Learning-Based Multimodal Fusion Approach for Predicting Acute Dermal Toxicity.

Acute dermal toxicity testing is essential for assessing the safety of chemicals used in pharmaceuticals, pesticides, cosmetics, and industrial chemicals. Conventional toxicity testing methods rely significantly on animal tests, which are resource-intensive and time-consuming and raise ethical issues. To address these issues and support the 3Rs principle (replacement, reduction, and refinement) in animal testing, this study investigates whether a multimodal deep learning framework based on the fusion of heterogeneous molecular representations can yield a reliable and accurate model for the prediction of acute dermal toxicity. This study proposes TriModalToxNet, a novel architecture that extracts features from three distinct molecular representations: 2D molecular images through a 2D convolutional neural network, SMILES embeddings via a 1D convolutional neural network, and molecular fingerprints via a fully connected neural network. These extracted features are then concatenated and passed into a deep neural network for classification. For comparative purposes, this study also evaluates BiModalToxNet, a baseline model using only 2D molecular images and fingerprints. The models are trained and tested on a curated data set consisting of 3845 compounds derived from experimental rat and rabbit acute dermal toxicity studies. The proposed model is evaluated using multiple standard performance metrics such as area under the receiver operating characteristic curve, sensitivity, Matthews correlation coefficient, and accuracy derived from stratified 10-fold cross-validation and external validation. TriModalToxNet achieved an area under the receiver operating characteristic curve of 95% and a sensitivity of 91.2% in cross-validation. External validation was also conducted to further demonstrate the robustness and generalizability of the model. These results show that multimodal methods can attain better predictive performance than traditional single-modality methods. This TriModalToxNet framework highlights the potential for integration into regulatory frameworks, pharmaceutical screening pipelines, and advancing the field toward more ethical and efficient chemical safety assessment.