Explainable Bidirectional Long Short-Term Memory Networks Learn Chemistry from SMILES for Predicting Toxicity of Androgen and Estrogen Receptor Chemicals

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Abstract
Endocrine disruption remains a major concern in predictive toxicology, demanding accurate and interpretable models to assess molecular interactions with hormonal pathways. Here, we present a descriptor-free bidirectional long short-term memory (BiLSTM) framework designed to predict the toxicity of chemicals toward androgen (AR) and estrogen receptors (ER), two of the most complex and biologically relevant end points in toxicology. The model operates directly on SMILES strings, which are tokenized and converted into one-hot encoded sequences, enabling the automatic extraction of chemically meaningful representations without reliance on handcrafted molecular descriptors or fingerprints. To enhance interpretability, we introduce a novel explainable artificial intelligence (XAI) approach that aggregates character-level attribution scores into color-coded substructures, revealing features that drive or reduce toxicity and offering mechanistic insight into receptor-mediated effects. The models were trained on publicly available, high-quality data sets comprising 1664 and 1529 chemicals with experimental binary labels for AR and ER, respectively. Employing cross-validation analyses, based on 20% randomly stratified resampling iterated 10 times, the proposed workflow returned accuracy equal to 0.75 ± 0.08 and 0.81 ± 0.05, sensitivity equal to 0.66 ± 0.36 and 0.69 ± 0.17, and specificity equal to 0.76 ± 0.14 and 0.82 ± 0.06 for AR and ER end points, respectively. Our descriptor-free models ensure highly transparent results with a substructure level interpretability. These findings demonstrate the potential of deep learning directly on molecular textual representations to advance predictive toxicology and to support mechanistic understanding in chemical risk assessment.

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