A Network Pharmacology-Based Approach to Investigate the Novel TCM Formula against Huntington's Disease and Validated by Support Vector Machine Model

Several pathways are crucial in Huntington's disease (HD). Based on the concept of multitargets, network pharmacology-based analysis was employed to find out related proteins in disease network. The network target method aims to find out related mechanism of efficacy substances in rational design way. Traditional Chinese medicine prescriptions would be used for research and development against HD. Virtual screening was performed to obtain drug molecules with high binding capacity from traditional Chinese medicine (TCM) database@Taiwan. Quantitative structure-activity relationship (QSAR) models were conducted by MLR, SVM, CoMFA, and CoMSIA, constructed to predict the bioactivities of candidates. The compounds with high-dock score were further analyzed compared with control. Traditional Chinese medicine reported in the literature could be the training set provided for constructing novel formula by SVM model. We tried to find a novel formula that can bind well with these targets at the same time, which indicates our design could be highly related to the HD. Additionally, the candidates would validate by a long-term molecular dynamics (MD) simulation, 5 microseconds. Thus, we suggested the herbs Brucea javanica, Holarrhena antidysenterica, Dichroa febrifuga, Erythrophleum guineense, etc. which contained active compounds might be a novel medicine formula toward Huntington's disease.