Principles of QSAR Modeling: Comments and Suggestions From Personal Experience

Principles of QSAR Modeling: Comments and Suggestions From Personal Experience

Paola Gramatica
DOI: 10.4018/IJQSPR.20200701.oa1
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At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.
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The fact that I started research in computational chemistry, particularly in QSAR modeling, after long experience at the University of Milano in a completely different field of chemistry, organic chemistry, carrying out experimental works on the synthesis and structural identification of natural products, certainly had relevant influence on how I dealt with the new research field in QSAR. Another fundamental element in my researches was to learn QSAR modeling from a leader in chemometrics and theoretical molecular descriptors, Prof. Roberto Todeschini of the University of Milano-Bicocca.

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