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Quantitative Structure-Activity Relationship (QSAR) Model Review
Published:
15 June 2020
by MDPI
in MOL2NET'20, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 6th ed.
congress USEDAT-06: USA-Europe Data Analysis Training Program Workshop, Bilbao, Spain-Cambridge, UK-Miami, USA, 2020
https://doi.org/10.3390/mol2net-06-06866
(registering DOI)
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)
Comments on this paper
Humbert G. Díaz
3 January 2021
QSAR & Machine Learning
Thank you for your participation. What are on your opinion the advantages/disadvantages of QSAR/QSPR techniques?
What are possible applications to Drug Discovery, Nanotechnology, and Fuel Industry?
What are possible applications to Drug Discovery, Nanotechnology, and Fuel Industry?
Juan Castillo-Garit
5 January 2021
Publication in QSAR
Very interesting analysis of the trends in publication with 'QSAR' as keyword.
Do you think that this behavior will continue?
Do you think that this behavior will continue?
Jose Bueso-Bordils
30 January 2021
QSAR review
Very interesting review on two of the methods used to find QSAR which I have used myself. I’d like to know your opinion on other QSAR methods, namely random decision forests and artificial neural networks. Thank you.
