Abstract
One of the primary tasks of analytical chemistry and QSAR/QSPR researches is building of prognostic regression equations based on descriptors sets. The one of the most important problems here is to decrease the number of descriptors in the initial descriptor set which is usually way too big. In current investigation the descriptor set is proposed to be reduced employing the least absolute shrinkage and selection operator (LASSO) approach. Decreased descriptor sets were used for calculations with application of the following QSAR/QSPR methods: ordinary least squares (OLS), the least absolute deviation (LAD) regressions and artificial neural networks (ANN). Contrary to aforementioned methods principal component regression (PCR) and partial least squares (PLS) approaches can produce solutions containing numerous descriptors. In this article we compared the viability of these two different descriptor handling ideologies in application to molecular chemical and physical properties prediction. From the obtained results it is possible to see that there are tasks for which PCR and PLS approaches can fail to produce accurate regression equations. At the same time, methods OLS and LAD that use small amount of descriptors can provide viable solutions for the same cases. It was shown that these small sets of descriptors selected with LASSO approach can be used in ANN to obtain models with even better internal validation characteristics.
Highlights
QSAR: Hansch analysis and related approaches, Methods and principles in medical chemistry, VCH Verlagsgesellschaft mbH, 1993.
Chemometrics in Food Chemistry, Data Handling in Science and Technology, Elsevier, 2013, 28(1), 512 p.
3. Roy K., Kar S., Das R.N., A Primer on QSAR/QSPR Modeling Fundamental Concepts, Springer briefs in molecular science, 2015.
Summary
QSAR: Hansch analysis and related approaches, Methods and principles in medical chemistry, VCH Verlagsgesellschaft mbH, 1993. Chemometrics in Food Chemistry, Data Handling in Science and Technology, Elsevier, 2013, 28(1), 512 p. 3. Roy K., Kar S., Das R.N., A Primer on QSAR/QSPR Modeling Fundamental Concepts, Springer briefs in molecular science, 2015. 4. Roy, K.; Advances in QSAR modeling Applications in Pharmaceutical, Chemical, Food, Agricultural and Environmental Sciences, Challenges and Advances in Computational Chemistry and Physics, 2017, Vol 24. 5. Gupta, S.P.; QSAR and Molecular Modeling Studies in Heterocyclic Drugs II, Topics in Heterocyclic Chemistry, 2006, Vol 4.
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