Francesca Cutropia

Toxicity prediction in small molecules represents a fundamental challenge in drug development and chemical safety assessment. Traditional approaches heavily rely on predefined molecular descriptors or fingerprints, potentially limiting the ability to capture complex and nonlinear structure–activity relationships. Here, we present a descriptor-free, language-inspired framework that can be applied to different toxicity prediction tasks within a unified architecture. The model proposed combines a multiscale Convolutional Neural Network (CNN) layer to capture chemical patterns at different scales and a Gated Recurrent Unit (GRU) layer to capture the sequential nature of these patterns. This architecture also exploits an attention mechanism that computes attention weights across the sequence, enabling the model to focus on the most relevant molecular substructures for toxicity prediction. Toxic and nontoxic chemicals, represented by canonical SMILES, are investigated as the words of two languages which have to be discriminated; using eight different end points, the model provided an accurate description of toxicity patterns, with an average Area under the ROC curve (AUC) of 0.83 (min: 0.70, max: 0.94) under repeated cross-validation. The models were trained on relatively small data sets (∼1000 samples) and often strongly imbalanced, two important challenges that highlight the opportunities for future improvement; moreover, the proposed attention-based framework offers a representation of the molecular regions influencing model predictions, providing a basis for future investigations into toxicity-related structural patterns and potentially supporting hypothesis generation in drug design or drug repurposing applications.

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.