Comparative QSAR- and Fragments Distribution Analysis of Drugs, Druglikes, Metabolic Substances, and Antimicrobial Compounds

A number of binary QSAR models have been developed using methods of artificial neural networks, k-nearest
neighbors, linear discriminative analysis, and multiple linear regression and have been compared for their
ability to recognize five types of chemical compounds that include conventional drugs, inactive druglikes,
antimicrobial substituents, and bacterial and human metabolites. Thus, 20 binary classifiers have been created
using a variety of ‘inductive' and traditional 2D QSAR descriptors which allowed up to 99% accurate
separation of the studied groups of activities. The comparison of the performance by four computational
approaches demonstrated that the neural nets result in generally more accurate predictions, followed closely
by k-nearest neighbors methods. It has also been demonstrated that complementation of ‘inductive' descriptors
with conventional QSAR parameters does not generally improve the quality of resulting solutions, conforming
high predictive ability of ‘inductive' variables. The conducted comparative QSAR analysis based on a novel
linear optimization approach has helped to identify the extent of overlapping between the studied groups of
compounds, such as cross-recognition of bacterial metabolites and antimicrobial compounds reflecting their
immanent resemblance and similar origin. Human metabolites have been characterized as a very distinctive
class of substances, separated from all other groups in the descriptors space and exhibiting different QSAR
behavior. The analysis of unique structural fragments and substituents revealed inhomogeneous scale-free
organization of human metabolites illustrating the fact that certain molecular scaffolds (such as sugars and
nucleotides) may be strongly favored by natural evolution. The established scale-free organization of human
metabolites has been contemplated as a factor of their unique positioning in the descriptors space and their
distinctive QSAR properties. It is anticipated that the study may bring additional insight into QSAR
determinants for conventional drugs, inactive chemicals, and metabolic substances and may help in
rationalizing design and discovery of novel antimicrobials and human therapeutics with improved, metabolite-like properties.