TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining.

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.

Graph convolutional network approach to investigate potential selective Limk1 inhibitors.

Since the Limk1 is a promising drug target and few inhibitors with good Limk1/ROCK2 selectivity have been reported, discovering potential and selective Limk1 inhibitors with novel scaffolds is becoming an urgent need to develop new treatments for the related diseases. Here, we utilized molecular docking to screen potential compounds of Limk1 from Traditional Chinese Medicine (TCM) database. Meanwhile, we performed a three-dimensional graph convolutional network (3DGCN), based on 3D molecular graph, to predict the inhibitory activity of Limk1 and ROCK2. Compared with the baseline models (RF, GCN and Weave), the 3DGCN achieved higher accuracy and the averaged RMSE values on test sets for Limk1 and ROCK2 were 0.721 and 0.852 respectively. In 3DGCN, above 80% of the test-set molecules from both two datasets were predicted within absolute error of 1.0 and the feature visualization suggested that it could automatically learn relevant structure features including 3D molecular information from a specific task for prediction. Furthermore, molecular dynamics (MD) simulations within 100 ns were employed to verify the stability of ligand-protein complexes and reveal the binding modes of the potential selective lead compounds of Limk1. Finally, integrating docking results, the predicted values by the 3DGCN and the MD analysis, we found that 7549 and 2007_15649 might be the potential and selective inhibitors for Limk1 receptor.