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Recently, Xiao Weilie's team from yunnan University's Ministry of Education's Key Laboratory of Natural Resources Pharmaceutical Chemistry published an article in the authoritative review journal Nature Product Reports, which reviewed the progress of research on the activity of natural products based on machine learning (IF s 11.876, https://doi.org/10.1039/D0NP00043D).
this paper, combined with the team's research experience, based on the summary of theoretical methods and analysis of literature cases, put forward a series of research recommendations, to establish a bridge between data scientists and drug researchers to establish an effective communication.
natural products are an important source of pharmaceutical molecules, but their structural discovery is "accidental" and biological activity exploration is "random", which limits the active discovery and in-depth study of natural products.
machine learning algorithm analyzes the molecular structure, physical and chemical properties and biological activity data of existing natural products, and establishes a predictive model of the activity of natural products.
compared with traditional methods, machine learning methods do not preset structural-active relationships based on existing knowledge, but filter out highly relevant parameters from thousands of descriptors to find nonlinear laws.
number of documents introducing machine learning methods in natural drug activity research is increasing year by year, but it is still constrained by a number of factors, such as the lack of highly integrated and standardized databases.
team began conducting the first systematic chemical informational studies on natural products of anti-inflammatory activity in 2018 (J. Chem. Inf. Model. 2019, https://doi.org/10.1021/acs.jcim.8b00560) and based on this research on the activity-target prediction methods and applications of natural products based on machine learning and network analysis.
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