This report describes a new set of molecular descriptors of relevance to QSAR/QSPR Studies and drug design, atom linear indices f(k)(x(i)). These atomic level chemical descriptors are based on the calculation of linear maps on R-n [f(k)(x(i)): (Rn-->Rn)] in canonical basis. In this context, the k(th) power of the molecular pseudograph's atom adjacency matrix [M-k(G)] denotes the matrix of f(k)(x(i)) with respect to the canonical basis. In addition. a local-fragment (atom-type) formalism was developed. The k(th) atom-type linear indices are calculated by summing the k(th) atom linear indices of all atoms of the same atom type in the molecules. Moreover, total (whole-molecule) linear indices are also proposed. This descriptor is a linear functional (linear form) on R-th. That is, the k(th) total linear indices is a linear map from R-n to the scalar R[f(k)(x): R-n-->R]. Thus, the k(th) total linear indices are calculated by summing the atom linear indices of all atoms in the molecule. The features of the k(th) total and local linear indices are illustrated by examples of various types of molecular Structures, including chain-lengthening, branching, heteroatoms-content, and multiple bonds. Additionally, the linear independence of the local linear indices to other 0D, 1D, 2D. and 3D molecular descriptors is demonstrated by using principal component analysis for 42 very heterogeneous molecules. Much redundancy and overlapping was found among total linear indices and most of the other Structural indices presently in use in the QSPR/QSAR practice. On the contrary, the information carried by atom-type linear indices was strikingly different from that codified in most of the 229 0D-3D molecular descriptors used in this study. It is concluded that the local linear indices are an independent indices containing important Structural information to be used in QSPR/QSAR and drug design studies. In this sense, atom, atom-type, and total linear indices were used for the prediction of pIC(50) values for the cleavage process of a set of flavone derivatives inhibitors of HIV-1 integrase. Quantitative models found are significant from a statistical point of view (R of 0.965, 0.902, and 0.927, respectively) and permit a clear interpretation of the Studied properties in terms of the structural Features of molecules. A LOO cross-validation procedure revealed that the regression models had a fairly good predictability (q(2) of 0.679, 0.543, and 0.721, respectively). The comparison with other approaches reveals good behavior of the method proposed. The approach described in this paper appears to be an excellent alternative or guides for discovery and optimization of new lead compounds.
