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- W3165697851 abstract "Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed." @default.
- W3165697851 created "2021-06-07" @default.
- W3165697851 creator A5029767937 @default.
- W3165697851 creator A5032019279 @default.
- W3165697851 creator A5048072583 @default.
- W3165697851 date "2021-01-01" @default.
- W3165697851 modified "2023-10-14" @default.
- W3165697851 title "Predicting biochemical and physiological effects of natural products from molecular structures using machine learning" @default.
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- W3165697851 doi "https://doi.org/10.1039/d1np00016k" @default.
- W3165697851 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34047331" @default.
- W3165697851 hasPublicationYear "2021" @default.
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