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.
Karakoc
Emre
E
School of Computing Science, Simon Fraser University, Burnaby, Canada.
Sahinalp
S Cenk
SC
Cherkasov
Artem
A
D000890:Anti-Infective Agents / Q000737:chemistry* / Q000494:pharmacology*; D016014:Linear Models; D008958:Models, Molecular; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
D000890:Anti-Infective Agents; D004364:Pharmaceutical Preparations