Combining yeast chemical genetics and AI to enable efficient identification of molecules from plants and fungi with cell inhibitory modes-of-action re

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Large living and preserved collections of plants and fungi, such as those found at Kew, represent an extremely valuable resource for drug discovery, but identifying pharmacologically selective molecules from nature that exhibit desirable modes-of-action is currently both time consuming and inefficient. Natural products chemistry needs new efficient approaches to unlock the medicinal potential of plant and fungal compounds and ensure that only compounds with exemplary pharmacological activity undergo chemical isolation. The aim of this project is to test the ability of an artificial intelligence cheminformatics platform to correctly predict which compounds out of all published plant and fungal metabolites, interact selectively with the cancer drug targets mTOR and HSP90 and the Parkinson's disease drug target -synuclein. By combining rapid in silico screening with simple, highly diagnostic yeast assays for these drug targets, we aim to demonstrate a way to sidestep the inefficient experimental approaches that are a current hallmark of the natural products field.