Discovery of Bioactive Constituents for Colitis From Traditional Chinese Medicine Prescription via Deep Neural Network
Zhixiang Ren, Yiming Ren, Pengfei Liu, Qi Shu, Huijuan Ma, Huan Xu · IEEE Transactions on Computational Biology and Bioinformatics · 2025
Colitis is a commonly encountered inflammatory disease in colon tissue, which can be triggered by various causes. Although a few ingredients in traditional Chinese medicine (TCM) have been identified as effective for the treatment of colitis, it remains a great challenge to discover the potential therapeutic bioactive constituents and their modes of action among thousands of ingredients in TCM prescriptions. To address this issue, we propose a pipeline that combines deep neural network (DNN) with network pharmacology to discover bioactive constituents. By integrating the herbal information network of 9,845 nodes and 161,950 edges, which includes detailed information on bioactive molecules and protein targets, with a prescription list expanded through a novel data augmentation strategy, the DNN can recommend diverse herbal combinations. Network pharmacology study revealed that the 10 most frequent constituents in recommended prescriptions were associated with multiple inflammatory signaling pathways. To verify the bioactive constituents in the recommended prescriptions, 5 selected constituents were administrated to BALB/c mice with colitis. Suppressive effects of disease progression and pro-inflammatory factors comparable to sulfasalazin were observed with these compounds, revealing the effectiveness of our artificial intelligence strategy in discovering bioactive constituents from TCM prescriptions.