DART Predictor: A Multi-Label Attention Model for High-Throughput Screening of Chemicals with Developmental and Reproductive Toxicity (DART)

Chemicals with developmental and reproductive toxicity (DART) pose significant risks to human health, particularly exposure during critical windows of embryonic and fetal development. Therefore, rapid and accurate identification of DART chemicals is urgently needed. Existing predictive models are predominantly limited to binary classification and lack explicit integration of exposure information, hindering the precise risk extrapolation across realistic exposure scenarios. Herein, we present DART Predictor, a multilabel deep learning model trained using a label-aware attention mechanism to predict six DART outcomes (Growth Disorders, Malformation, Fetal Viability Loss, Maternal Systemic Toxicity, Maternal Pathology, and Fertility Impairment). Trained on 25,175 chemically diverse records integrating molecular descriptors and bioassay activity features calibrated with exposure parameters, DART Predictor achieves state-of-the-art performance (average AUC: 0.964, average recall: 0.923) and strong interpretability and generalizability (AUC: 0.889, recall: 0.959) on two external validation data sets. The exposure parameters enhance model performance by up to 8.6% gain of AUC across multiple DART outcomes, indicating the vital role of realistic exposure information for model improvement. DART Predictor is further deployed into a cloud platform (http://www.ai4environ.cn/dartpredictor) to provide high-throughput screening service. Our study provides a novel framework for exposure-informed DART risk assessment, advancing the development of DART-related new approach methodologies.