Artificial Intelligence Based Discovery of Estrogen Receptor Activation Function 2 (AF2) Inhibitors as the First-In-Class Therapies for Drug Resistant Breast Cancers

Principal Investigator: 
Award Type: 

Breast cancer (BCa) is the most common cancer and the second cause of death from cancer among Canadian women. While antiestrogens are effective initially, BCas eventually reach a state where they no longer respond to conventional treatments. In a first effort to develop new drugs for resistant BCas, we developed inhibitors with a novel mechanism of action, able to suppress the proliferation of BCa cell lines that do not respond to standard therapies. While promising, better compounds are required for effective treatment of resistant BCa.

Chemical libraries already contain more than one billion of compounds, starting a new era of computer-aided drug discovery. Unfortunately, screening of such amount of chemicals is not yet possible using standard methods due to the required computational resources. To overcome this limit, we have developed an artificial intelligence method, progressive docking, which allows to virtually screen such libraries for the first time ever. In this way, we will be able to discover new inhibitors by evaluating billions of available compounds, in order to improve the outcome of BCa for women in Canada and worldwide.

Host Institution: 
University of British Columbia
Research Location: 
University of British Columbia – Vancouver Campus
Supervisor: 
Artem Cherkasov
Year: 
2020