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| About Artem Cherkasov (University of British Columbia) |
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Dr. Cherkasov is an Assistant Professor in the Faculty of Medicine at the University of British Columbia. He joined the Infectious Diseases Division at the UBC Faculty of Medicine in February 2003 where his research focuses on developing and applying bioinformatics tools in drug design, molecular modeling, and bioinformatics.
He received undergraduate and graduate training in chemistry from Kazan State University (Kazan, Russia). Dr. Cherkasov obtained his Ph.D. in 1996 from the Russian Academy of Science. In 2001 Dr. Cherkasov became one of the youngest recipients (29 y/o) of the Russian Academy of Science’s secondary level Doctor of Science degree, typically a late-stage-career achievement.
From 1998 until 2000, Dr. Cherkasov held postdoctoral positions in the Royal Institute of Technology in Stockholm, Sweden, and the University of Saskatchewan, Canada. In 2001 he joined the BC Cancer Research Centre’s BC Genome Sciences Centre as a Research Associate.
Dr. Cherkasov’s research interests include computer-aided drug design, applications of artificial intelligence in structure-activity modeling for bioactive substances, development of large-scale bioinformatics and genomics tools and molecular modeling techniques. He is the author of more than 100 scientific articles in peer reviewed journals, conference proceedings and patents.
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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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