Compound recommendation for plant health using machine learning and computational chemistry

Virtual screening is a computational technique used in drug discovery to search large libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. Virtual screening is thought to have the potential to speed the rate of discovery by reducing the need for expensive and time-consuming lab tests to physically test thousands of diverse compounds, often with an expected hit rate on the order of 1% or less with still fewer expected to be real leads following further testing. The proposed project aims to develop computational models that would screen through large libraries of chemical compounds and recommend those with potential efficacy against desired indications in plants and crops. The models will be developed using data collected by Terramera, with high classification accuracy, according to threshold tolerances defined by Terramera scientists.

Intern: 
Qingyuan Feng
Faculty Supervisor: 
Martin Ester
Province: 
British Columbia
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