Computer-aided Drug Design Based On Traditional Chinese Medicines

In recent years, many drugs approved by the Food and Drug Administration (FDA)directly come from natural products. As an important source of natural products,traditional Chinese medicines (TCMs) are gaining more and more attention in moderndrug discovery pipelines. The classic TCMs are primarily based on a large number ofherbal formulae that are used for the treatment of a wide variety of diseases. It isbelieved that TCMs are a good source of drug-like compounds. Discovery of newbioactive compounds from herbs used in TCMs and identification of theirpharmacological effects are becoming a promising way for finding new drugs in thepharmaceutical industry. However, until now the in-depth analyses of compoundsidentified in TCMs are still lacking. For example, we do not have in-depthunderstanding about the characteristics of the physicochemical properties, structures anddrug-likeness of the compounds in TCMs. Besides, compared with theory of westernmedicine treatment, the mechanism of TCMs for curing disease is not clear. Uncoveringthe underlying action mechanisms of TCMs for combating diseases at the molecularlevel is an important topic. At last, how to identify promising active compounds moreeffective for targets of interest is also a research hotspot.In order to promote the development and modernization of TCMs, the systematicalstudies on the computer-aided drug design (CADD) based on the compounds in TMCswere reported in our thesis. First, the molecular properties and structural features amongthe drug-like compounds in MDDR, the non-drug-like compounds in ACD and thenatural compounds in TCMCD were investigated systematically. The resultsdemonstrated that, compared with the compounds in MDDR and ACD, the naturalcompounds in TCMCD had more diverse property distribution, novel and morecomplex structural features. In addition, the drug-likeness filters based on simplemolecular properties and/or structural features are unreliable and have low predictionaccuracy. In order to construct more reliable theoretical models for drug-likeness andevaluate the drug-likeness of TCMCD, machine learning techniques, including na ve Bayesian classification and recursive partitioning methods were used. The drug-likenessmodels based on molecular physicochemical properties cannot give satisfactoryprediction accuracy. By adding molecular fingerprints, the prediction power can beimproved substantially. Besides, it can be found that the prediction accuracy of thedrug-likeness model is closely related to the size and the balance degree of the trainingset. Then, the best drug-likeness model to employed to evaluate the drug-likeness of thecompounds in TCMCD and found that more than60%compounds were predicted to bedrug-like. The results indicated that the TCMCD is drug-like statistically and believedto be a good source of drug-like compounds.It is well known that basic form of TCMs for curing diseases is TCM formulae (orprescriptions), which is a mixture of special herbs. Therefore, it is not clear that how alarge number of chemical compounds of TCM formulae combat diseases. In order tounderstand the interaction mechanism of TCM formulae at the molecular level, weinvestigated the theory of TCM formulae for treating type2diabetes (T2DM). First, wecollected the T2DM related targets and the chemical compounds in TCM formulae fortreating T2DM. By employing structure-based virtual screening approaches includingmolecular docking, pharmacophore mapping, and machine learning approaches toidentify potential active compounds for T2DM targets. Then, we built the interactionnetwork between the potential active compounds and T2DM related targets. Byanalyzing the compound-target network, we can conclude the mechanism of TCMformulae for curing T2DM as follows: most chemical compounds in TCM formulae canonly interact with an individual target, forming the leading fighting force to combatT2DM. Then, those potential multi-target compounds may influence the T2DM-relatedtargets, forming additional forces to enhance the therapeutic effects. At last, a portion ofthe compounds are responsible for remedying the other related symptoms that areproved to be related to T2DM, such as free radical scavenging/antioxidant andantibacterial activities. All of these observations can be seen as a proper way to revealthe classical theory “Monarch, Minster, Assistant, and Guide” in TCM prescriptions atthe molecular level.In order to identify potential active compounds from TCMCD for targets ofinterest and considering the influence of protein flexibility in virtual screening, we havedesigned and evaluated a parallel virtual screening protocol by integrating the predictionresults from molecular docking and complex-based pharmacophore searching based onmultiple protein structures of ROCK1. It is encouraging to find that the integrated classifiers illustrate much better performance than molecular docking or complex-basedpharmacophore searching based on any single ROCK1structure. Then, the most reliableclassifier was utilized to identify potential inhibitors of ROCK1from TCMs. Thepotential active compounds are novel compared with the known ROCK1inhibitors, andthey can be served as promising starting points for the development of ROCK1inhibitors. The novel parallel VS strategy developed here is quite reliable and can beused as a powerful tool in drug screening.