Modeling properties of biochemical compounds with connectivity terms

The descriptive and utility power of linear combinations of connectivity terms (LCCT) derived by a trial-and-error procedure from a medium-sized set of 8 connectivity indices: {χ} = {D, D^sup v^,^sup 0^ χ,^sup 0^ χ^sup v^,^sup 1^ χ,^sup 1^ χ^sup v^,χ^sub t^,χ^sub t^^sup v^} or from a subset of it has been tested on properties of heterogeneous classes of biochemical compounds centered on the homogeneous class of natural L-amino acids. To choose the appropriate combination of indices the forward selection and the complete combinatorial technique have been used, whenever more than a single term was necessary for the description. The forward selection technique searches only a subspace of the complete combinatorial space, but nevertheless has many advantages among which to be a good tool for an elementary and direct test for newly defined indices. The modeling has been followed centering the attention not only on the predictive power of the proposed linear equations but also on their utility. The modeling of the solubility of the entire heterogeneous class of n = 43 amino acids, purities and pyrimidines could satisfactorily be achieved with a set of supraconnectivity terms based on theχ^sub t^^sup v^ index mainly. The unfrozen water content of a mixed class of inorganic salts and natural amino acids has satisfactorily been modeled with two connectivity terms and the modeling shows a remarkable utility. The utility of the given LCCT can nevertheless be enhanced, especially when the modeling requires 2 or more terms, with the introduction of the corresponding orthogonal indices, as can be seen for S(AA + PP) and UWC. Further, theδ cardinal number is used as starting point for the definition of a supravalence index Δ to be used for a topological codification of the genetic code and the amino acids in proteins. In fact, the notion of supravalence can be extended to the triplet code words to generate the different families and subfamilies of the genetic code and to visualize the connections of amino acids in proteins. Three properties of the DNA-RNA bases (U, T, A, G and C), the singlet excitation energies ΔE^sub 1^ and δE^sub 2^, and the molar absorption coefficient ε^sub 260^ have been simulated with a single connectivity term chosen from the same medium-sized set of 8 molecular connectivity indices.[PUBLICATION ABSTRACT]