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Short Communication

Molecules Absorption Prediction Using Support Vector, Adaboost, Random Forest and Decision Tree Classification

Journal of Biomedical Research & Environmental Sciences article abstract with citation details, DOI, publication dates, subject areas, full text links, and references.

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Open Access
Article Type Short Communication
Subject Medicine Group
OCLC 9501820229
Suprapto Suprapto* and Yatim Lailun Nimah
Issue: Volume3-Issue3
Pages: 277-282
Received: 2022-03-18
Accepted: 2022-03-22
Published: 2022-03-22

Abstract

Classification is supervised machine learning applicable to predict chemicals based on their properties. The chemical properties are derived from its structural and functional groups. Many molecular descriptors have been developed, one of which, was pharmacophore. Pharmacophore is a quantitative measure of molecules in their application as a pharmaceutical ingredient. The training datasets were 59 molecules categorized on their adsorption properties. The classification was carried out to divide the training set into their adsorption class using their pharmacophores. The prediction of enolic curcumin and its degradation product was used to verify the trueness of classification methods based on their pharmacophores. Curcumin and its degradation product were used because there were many studies carried out about curcumin and its pharmaceutical effect.

FullText HTML FullText PDF DOI: 10.37871/jbres1433 Crossmark

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Copyright

© 2022 Suprapto S, et al. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

Suprapto S, Ni’mah YL. Molecules Absorption Prediction Using Support Vector, Adaboost, Random Forest and Decision Tree Classification. J Biomed Res Environ Sci. 2022 Mar 22; 3(3): 277-282. doi: 10.37871/jbres1433, Article ID: JBRES1433, Available at: https://www.jelsciences.com/articles/jbres1433.pdf

Subject area(s)

Pharmaceutica Analytica Acta

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