On the optimal selection of principal components in QSPR studies

A heuristic method to sort principal components is analysed. The obtained a rrangements are property dependent and it is demonstrated how the procedure is equivalent to the called Most Predictive Variable Method. As an applica tion of the new algorithm, a Quantitative Structure-Property Relationships (QSPR) study is performed over the set of the 18 structural isomers of the octane molecule. The original molecular descriptors are obtained from a qua ntum similarity matrix related to the molecular family. The analysis is bas ed on the use of linear models where distinct sets of principal components act as optimal descriptors for 6 physicochemical molecular properties. The proposed algorithm allows to determine sequences of the first Principal Com ponents which are identified as forming the optimal descriptors set for eac h of the 6 studied properties. The benefits of the new approach are reveale d when comparing the obtained results with classical ones arising from a st andard principal component analysis study.