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.