Protecting your skin: a highly accurate LSTM network integrating conjoint features for predicting chemical-induced skin irritation

Skin irritation is a significant adverse effect associated with chemicals and drug substances. Quantitative structure-activity relationship (QSAR) is an alternative method bypassing in vivo assay for filling data gaps in chemical risk assessment. In this study, we developed QSAR models based on recurrent neural networks (RNNs) to classify skin irritation caused by chemical compounds. We utilized chemical language notation, molecular substructures, molecular descriptors, and a combination of these features named conjoint fingerprints for model construction. A simple RNN, long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent units (GRU), and bidirectional gated recurrent units (BiGRU) architectures were used to build the QSAR models. We found that the LSTM and a combination of molecular fingerprints and descriptors outperformed the other models significantly with 80% accuracy, 60% MCC, and 85% AUC for the external test set evaluation. Thereby, we selected this model for generalizability testing with other test sets beyond our study, ensuring that the model can be used with other data sets. Furthermore, the applicability domain of the purposed model was developed, enabling a trustable prediction will be made for a test compound. This model was developed based on OECD guidelines for skin irritation assessment and QSAR model development, assuring compliance with all required standards. The models and source codes developed in this study are publicly available, facilitating chemical design and safety evaluation, particularly for assessing the skin irritation potential of chemicals.