Mulaqua: An interpretable multimodal deep learning framework for identifying PMT/vPvM substances in drinking water

Drinking water is an essential resource for human health and well-being, still it faces increasing threats from contamination by chemical pollutants. Among the contaminants, persistent, mobile, and toxic (PMT) substances, along with very persistent and very mobile (vPvM) substances, have emerged as chemicals of significant concern due to their harmful effects on human health. Regulatory bodies have recognized them as emerging contaminants requiring stricter monitoring and management practices. Traditional experimental methods for detecting and characterizing these substances are often slow and resource-intensive. Therefore, there is a pressing need to develop efficient computational approaches to detect persistent, mobile, and toxic, or very persistent and very mobile (PMT/vPvM) substances rapidly and economically. Addressing this gap, we proposed Mulaqua, the first deep learning (DL) approach specifically designed for identifying PMT/vPvM substances. Mulaqua utilizes a novel multimodal approach combining molecular string representation with molecular image for the final prediction. To address the data imbalance issue in the training dataset, we employ a data augmentation strategy based on Simplified Molecular Input Line Entry System (SMILES) enumeration, which helped to achieve a balanced performance with the training accuracy (ACC), F1-score (F1), and Matthews correlation coefficient (MCC) score of 0.920, 0.590, and 0.548, respectively. Our study also includes interpretability analyses to elucidate how specific molecular architectures influence PMT/vPvM substances characterization, thereby providing meaningful insights. Mulaqua demonstrates excellent transferability, validated through rigorous evaluation of external datasets, which significantly improves performance compared to the baseline. Unlike previous methods, Mulaqua is now publicly available at https://github.com/cbbl-skku-org/Mulaqua/, holds significant potential as a proactive tool for early hazard identification and regulatory prioritization of PMT/vPvM substances in environmental risk management.