1.Introduction
Parkinson’s disease(PD) is a neurological disorder with evolving layers of complexity1, which is more common in the elderly. Knowledge regarding the pathophysiological basis of Parkinson’s disease (PD) has been greatly expanded over the past two decades, with extraordinary contributions from the field of genetics2.The average age of onset is about 60 years old. The exact cause of this pathological change is still unknown1. Risk of developing Parkinson’s disease is no longer viewed as primarily due to environmental factors1. Genetic factors, environmental factors, aging, oxidative stress and other factors may be involved in the degeneration and death process of PD dopaminergic neurons. Previous studies have shown that small molecule inhibitors of NLRP3 may be a potential treatment for Parkinson’s disease3. The NLRP3 inflammasome participates in the pathogenesis of PD and that inhibiting the downstream pathway of the NLRP3/caspase-1/IL-1β axis can alleviate the occurrence of PD symptoms4. Inhibition of hepatic NLRP3 inflammasome weakens inflammatory cytokines spreading into the brain and delays the progress of neuro inflammation and DA neuronal degeneration3.In the past few years we have developed computer-aided drug design servers5. There two things that we can effectively screen new compounds for many diseases. For conducting online virtual screening and new drug design6, we used IScreen. At the same time, based on TCM database (TCM Database @Taiwan)7,8, ISMART can be used for computer-aided drug design. Traditional Chinese medicine (TCM) has a long history of viewing an individual or patient as a system with different statuses, and has accumulated numerous herbal formulae9. Currently pharmacologic dogma, ”single drug, single target, single disease”, is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement the established guiding pharmacologic approaches10. Viewing drug action through the lens of network biology may provide insights into how we can improve drug discovery for complex diseases11. Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization12Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge13.Similarly, random forests can also be applied in biomedicine14QSAR (quantitative structure-activity relationship) is a method for predicting the physical and biological properties of small molecules; it is today in large use in companies and public services15. A variety of artificial intelligence methods can be used to build QSAR models, such as Multiple linear regression (MLR)16, support vector machine (SVM)17, Comparative force field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA) models .It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. Although the trial and error factor involved in the development of a new drug cannot be ignored completely, QSAR certainly decreases the number of compounds to be synthesized by facilitating the selection of the most promising candidates. Several success stories of QSAR have attracted the medicinal chemists to investigate the relationships of structural properties with biological activity18. In this study, network pharmacology was used to search for target proteins related to Parkinson’s syndrome. We used molecular docking technology to screen suitable small molecules from the database of Chinese herbal medicines. Several methods such as 2d-QSAR and 3d-QSAR are used to analyze their biological activity. Last but not least, w conducted molecular dynamics simulation of these compounds and finally identified the candidate compounds. The specific process is shown in the Figure 1.