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Introduction: As a new discipline, network pharmacology has been widely used to disclose the material basis and mechanism of Traditional Chinese Medicine in recent years. However, numerous researches indicated that the material basis of TCMs identified based on network pharmacology was the mixtures of beneficial and harmful substances rather than the real material basis. In this work, taking the anti-NAFLD (non-alcoholic fatty liver disease) effect of Bai Shao (BS) as a case, we attempted to propose a novel bioinformatics strategy to uncover the material basis and mechanism of TCMs in a precise manner. Methods: In our previous studies, we have done a lot work to explore TCM-induced hepatoprotection. Here, by integrating our previous studies, we developed a novel computational pharmacology method to identify hepatoprotective ingredients from TCMs. Then the developed method was used to discover the material basis and mechanism of Bai Shao against Non-alcoholic fatty liver disease by combining with the techniques of molecular network, microarray data analysis, molecular docking, and molecular dynamics simulation. Finally, literature verification method was utilized to validate the findings. Results: A total of 12 ingredients were found to be associated with the anti-NAFLD effect of BS, including monoterpene glucosides, flavonoids, triterpenes, and phenolic acids. Further analysis found that IL1-β, IL6, and JUN would be the key targets. Interestingly, molecular docking and molecular dynamics simulation analysis showed that there indeed existed strong and stable binding affinity between the active ingredients and the key targets. In addition, a total of 23 NAFLD-related KEGG pathways were enriched. The major biological processes involved by these pathways including inflammation, apoptosis, lipid metabolism, and glucose metabolism. Of note, there was a great deal of evidence available in the literature to support the findings mentioned above, indicating that our method was reliable. Discussion: In summary, the contributions of this work can be summarized as two aspects as follows. Firstly, we systematically elucidated the material basis and mechanism of BS against NAFLD from multiple perspectives. These findings further enhanced the theoretical foundation of BS on NAFLD. Secondly, a novel computational pharmacology research strategy was proposed, which would assist network pharmacology to uncover the scientific connotation TCMs in a more precise manner., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 He, Chen, Yi, Hou, Fu, Xie, Zhang, Liu, Ru and Wang.)

Motivation: The metabolites of exogenous and endogenous compounds play a pivotal role in the domain of metabolism research. However, they are still unclear for most chemicals in our environment. The in silico methods for predicting the site of metabolism (SOM) are considered to be efficient and low-cost in SOM discovery. However, many in silico methods are focused on metabolism processes catalyzed by several specified Cytochromes P450s, and only apply to substrates with special skeleton. A SOM prediction model always deserves more attention, which demands no special requirements to structures of substrates and applies to more metabolic enzymes., Results: By incorporating the use of hybrid feature selection techniques (CHI, IG, GR, Relief) and multiple classification procedures (KStar, BN, IBK, J48, RF, SVM, AdaBoostM1, Bagging), SOM prediction models for six oxidation reactions mediated by oxidoreductases were established by the integration of enzyme data and chemical bond information. The advantage of the method is the introduction of unlabeled SOM. We defined the SOM which not reported in the literature as unlabeled SOM, where negative SOM was filtered. Consequently, for each type of reaction, a series of SOM prediction models were built based on information about metabolism of 1237 heterogeneous chemicals. Then optimal models were attained through comparisons among these models. Finally, independent test set was used to validate optimal models. It demonstrated that all models gave accuracies above 0.90. For receiver operating characteristic analysis, the area under curve values of all these models over 0.906. The results suggested that these models showed good predicting power., Availability and Implementation: All the models will be available when contact with wangyun@bucm.edu.cn., Contacts: wangyun@bucm.edu.cn or yjqiao@yahoo.com., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)