Nguyen Doan Hieu Nguyen

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.

Bitter peptides (BPs), derived from the hydrolysis of proteins in food, play a crucial role in both food science and biomedicine by influencing taste perception and participating in various physiological processes. Accurate identification of BPs is essential for understanding food quality and potential health impacts. Traditional machine learning approaches for BP identification have relied on conventional feature descriptors, achieving moderate success but struggling with the complexities of biological sequence data. Recent advances utilizing protein language model embedding and meta-learning approaches have improved the accuracy, but frequently neglect the molecular representations of peptides and lack interpretability. In this study, we propose xBitterT5, a novel multimodal and interpretable framework for BP identification that integrates pretrained transformer-based embeddings from BioT5+\thinspacewith the combination of peptide sequence and its SELFIES molecular representation. Specifically, incorporating both peptide sequences and their molecular strings, xBitterT5 demonstrates superior performance compared to previous methods on the same benchmark datasets. Importantly, the model provides residue-level interpretability, highlighting chemically meaningful substructures that significantly contribute to its bitterness, thus offering mechanistic insights beyond black-box predictions. A user-friendly web server (https://balalab-skku.org/xBitterT5/) and a standalone version (https://github.com/cbbl-skku-org/xBitterT5/) are freely available to support both computational biologists and experimental researchers in peptide-based food and biomedicine.