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  1. Home
  2. Our Research
  3. Artem Cherkasov

Artem Cherkasov

Professor
Academic Appointment
Professor
Research Focus
Kidneys and Urinary System Cancers
Contact Info
acherkasov@prostatecentre.com
604-875-4111 x 69628
https://ca.linkedin.com/in/artem-cherkasov-155165b

Biography

Dr. Artem Cherkasov is Tier 1 Canada Research Chair in Precision Cancer Drug Design. He is a professor in the Department of Urologic Sciences at the University of British Columbia and senior scientist at the Vancouver Prostate Centre. His research interests include computer-aided drug discovery (CADD), structure-activity modeling, drug reprofiling, new cancer therapeutics discovery and development of novel CADD tools and applications.

News and Awards

AI innovation puts VCHRI scientist at the forefront of global drug discovery avenues

Apr 8, 2025

Milestone in AI technology identifies viable COVID-19 medication prototypes

Nov 30, 2021

A novel approach to outsmart prostate cancer

Nov 5, 2018

Outrunning disease: a new key to unlock the relationship between our genes and drugs

Nov 18, 2017

Technology and expertise align, resulting in novel potential treatment for lethal prostate cancer

Feb 4, 2016

Potential prostate cancer treatment developed in B.C. gets big boost

Dec 15, 2015

People in profile: Miriam Butler

Jan 26, 2015

Publications

  • European journal of medicinal chemistry -

    Computer-aided drug discovery of Myc-Max inhibitors as potential therapeutics for prostate cancer.

    Carabet LA, Lallous N, Leblanc E, Ban F, Morin H, Lawn S, Ghaidi F, Lee J, Mills IG, Gleave ME, Rennie PS, Cherkasov A
  • Cancer letters -

    Selectively targeting the dimerization interface of human androgen receptor with small-molecules to treat castration-resistant prostate cancer.

    Dalal K, Ban F, Li H, Morin H, Roshan-Moniri M, Tam KJ, Shepherd A, Sharma A, Peacock J, Carlson ML, LeBlanc E, Perez C, Duong F, Ong CJ, Rennie PS, Cherkasov A
  • Journal of chemical information and modeling -

    Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images.

    Fernandez M, Ban F, Woo G, Hsing M, Yamazaki T, LeBlanc E, Rennie PS, Welch WJ, Cherkasov A
  • ACS central science -

    Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery.

    Gentile F, Agrawal V, Hsing M, Ton AT, Ban F, Norinder U, Gleave ME, Cherkasov A
  • Molecular informatics -

    Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.

    Ton AT, Gentile F, Hsing M, Ban F, Cherkasov A
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