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Atom, atom-type, and total linear indices of the "molecular pseudograph's atom adjacency matrix": Application to QSPR/QSAR studies of organic compounds

Abstract:

In this paper we describe the application in QSPR/QSAR studies of a new group of molecular descriptors: atom, atom-type and total linear indices of the molecular pseudograph's atom adjacency matrix. These novel molecular descriptors were used for the pbkp_rediction of boiling point and partition coefficient (log P), specific rate constant (log k), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes, respectively. For this purpose two quantitative models were obtained to describe the alkyl-alcohols' boiling points. The first one includes only two total linear indices and showed a good behavior from a statistical point of view (R2 = 0.984, s = 3.78, F = 748.57, q2 = 0.981, and scv = 3.91). The second one includes four variables [3 global and 1 local (heteroatom) linear indices] and it showed an improvement in the description of physical property (R 2 = 0.9934, s = 2.48, F = 871.96, q2 = 0.990, and s cv = 2.79). Later, linear multiple regression analysis was also used to describe log P and log k of the 2-furyl-ethylenes derivatives. These models were statistically significant [(R2 = 0.984, s = 0.143, and F = 113.38) and (R2 = 0.973, s = 0.26 and F = 161.22), respectively] and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment [(q2 = 0.93.8 and scv = 0.178) and (q2 = 0.948 and scv = 0.33), respectively]. Finally, a linear discriminant model for classifying antibacterial activity of these compounds was also achieved with the use of the atom and atom-type linear indices. The global percent of good classification in training and external test set obtained was of 94.12% and 100.0%, respectively. The comparison with other approaches (connectivity indices, total and local spectral moments, quantum chemical descriptors, topographic indices and Estate/biomolecular encounter parameters) reveals a good behavior of our method. The approach described in this paper appears to be a very promising structural invariant, useful for QSPR/QSAR studies and computer-aided "rational" drug design.