EventsMOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Event submissions
Published
with-doi10.3390/mol2net-06-06866 (registering DOI)
This submission belongs to the session 09. USEDAT-06: USA-Europe Data Analysis Training Program Workshop, Bilbao, Spain-Cambridge, UK-Miami, USA, 2020 of the event MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
Published date
15 Jun, 2020
Citation
Harbil Bediaga, Quantitative Structure-Activity Relationship (QSAR) Model Review, in Proceedings of MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed., 30 January 2020–30 January 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-06-06866
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Quantitative Structure-Activity Relationship (QSAR) Model Review

Harbil Bediaga 1
1. Department of Organic Chemistry II, Faculty of Science and Technology, University of Basque Country (UPV/EHU)
Abstract

The Quantitative Structure-Activity Relationship (QSAR) models are a very useful tool in the design of new chemical compounds. The QSAR methods are based on the assumption that the activity of a certain chemical compound is related to its structure. Two types of QSAR analysis are summarized in this review: Linear Regression model (LR) and Linear Discriminant Analysis model (LDA).

Keywords
QSAR
Predictive Models
Linear Discriminant Analysis (LDA)
Linear Regression (LR)
Manuscript
Quantitative Structure-Activity Relationship (QSAR) Model Review.pdf
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