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| About Artem Cherkasov (University of British Columbia) |
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Drugs, Drug-Likeness, Metabolism, and Antimicrobals
Artem Cherkasov, University of British Columbia, Canada
We have developed a series of binary QSAR models utilizing methods of the Artificial Neural Networks, k-Nearest Neighbors, Linear Discriminative Analysis and Multiple Liner Regression for classification of five types of chemical compounds that include conventional drugs, inactive drug-likes, antimicrobial substituents and bacterial- and human metabolites. Thus, a number of binary classifiers have been created using a variety of ‘inductive’ and traditional 2D QSAR descriptors that allowed up to 99% accurate separation of the studied groups of activities. The consequent comparative QSAR analysis allowed sampling 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.
The developed QSAR models for ‘antibiotic-like’ ‘bacterial-metabolite-like’ potential of compounds have further been utilized to identify several antibiotic candidates from the collection of conventional drug and drug-like substances.
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