MODELING PURINES AND PYRIMIDINES WITH THE LINEAR COMBINATION OF CONNECTIVITY INDEXES - MOLECULAR CONNECTIVITY LCCI-MC METHOD

A series of five experimental properties of DNA-RNA bases (U, T, A, G, and C): singlet excitation energies Delta E(1) and Delta E(2), oscill ator strengths f(1) and f(2), and molar absorption coefficient epsilon (260) plus three experimental properties of a wider set of purine and pyrimidine bases: average (pK) = 1/2 (pK(a) + pK(b)), molecular weight s MW, and solubility have been simulated in two different ways with li near combinations of connectivity indices (LCCI) chosen from a medium sized molecular connectivity {chi} = {D,D-v,(0) chi,(0) chi(v),(1) chi ,(1) chi(v),chi(t),chi(t)(v)} set. A forward selection technique and a full combinatorial space technique have been used to choose the best linear combination of connectivity indices for an optimal modeling. Th e given properties are very well modeled with the only exception being (pK), whose modeling could be improved with the introduction of fragm ent reciprocal connectivity indices, that take into account the number of basic and acid groups of the given molecules. The (easier to perfo rm) forward selection technique is in many occasions a good alternativ e to the more cumbersome full space selection technique and can normal ly be used to restrict the dimension of the full combinatorial space, thus, facilitating the computation. Limits in the forward selection me thod can frequently be overcome with the introduction of orthogonal in dices. While the simulation of the molecular weights cast some light o n the modeling of hydrogen-rich or -poor molecules, the simulation of the solubility shows (i) how far a satisfactory modeling of a small nu mber of compounds can be extrapolated by the aid of the same indices t o a wider set, (ii) the importance of linear combinations of squared c onnectivity indices used in the absolute value mode, and (iii) the con tribution of supramolecular connectivity indices for an improved model ing of the solubility. The positive role of the chi(t) and chi(1)(v) i ndices, all along the modeling of the different properties, seems to b e due to the rather low collinearity of these indices relative to the other indices of the connectivity set, a fact that underlines their us e in molecular modeling with linear combinations of connectivity indic es. In an Appendix, the notation of delta cardinal number is extended to the triplet code words to generate the different families and subfa milies of the genetic code.