Application of orthogonal L-shaped PLS to chemogenomics data and its chemical interpretation from predictive and orthogonal regression coefficients

• Orthogonal LPLS (OLPLS) was applied to the data set of human ABC transporters.
• OLPLS is suitable for detecting the predictive and orthogonal chemical parts.
• The predictive and orthogonal chemical parts were expressed in atom-colorings.
• The orthogonal chemical parts are roughly reverse to the predictive ones.
• Structural and chemical informatics would be further integrated for effective molecular design.

In the orthogonal LPLS (OLPLS) method, the scores and loadings of the latent variables can be transformed to the regression coefficient value of each descriptor. Furthermore, the regression coefficient values are expressed in the atom-coloring method. That is, the fragments and atoms are colored by the signs and values of the regression coefficients. Both computational and medicinal chemists make cooperatively molecular design using the common language of chemical structures and atom colors. In this paper, we examined the possibility of the clear chemical interpretation using the human ATP-binding cassette (ABC) transporters inhibitory data set. The full data set was generated using the pair-wise kernel regression method. The generated score matrix was analyzed by the ECFP_6 fingerprints and the z-scales derived from the chemical structures and the amino acid residues in the active sites of human ABC transporters. The result of atom-coloring for predictive chemical parts of an inhibitor was examined by the human ABCB1 transporter homology model based on the mouse X-ray crystal structure. It was well matched to strong hydrophobic sites within the active site. The originality of this paper has two folds: the regression coefficients in the OLPLS model are expressed by the atom-coloring method and the chemical interpretation for inhibitors is rigorously validated by the human ABC transporter homology model.