On the optimal selection of principal components in QSPR studies

A heuristic method to sort principal components is analysed. The obtained arrangements are property dependent and it is demonstrated how the procedure is equivalent to the called Most Predictive Variable Method. As an application 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 quantum similarity matrix related to the molecular family. The analysis is based 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 Components which are identified as forming the optimal descriptors set for each of the 6 studied properties. The benefits of the new approach are revealed when comparing the obtained results with classical ones arising from a standard principal component analysis study.