NPIF Phenotypic virtual screening - exploiting machine learning techniques to derive predictive models from high content screening datasets

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Traditional discovery methods to identify and validate new therapeutic targets has been undergoing a drastic decline in effectiveness, despite greater resources and more modern techniques being applied. A surprising rise in the development of first in class therapeutics has come from phenotypic screening efforts, perhaps able to capture complex systems biology and network effects in the final readout of cellular response, and drawing attention only to efficacious compounds [1].The Auer and Carragher labs at the University of Edinburgh are in a unique position to apply machine learning techniques to an existing high content phenotypic dataset for the creation of a new field of virtual screening. Traditionally, there have been two branches; structure- based, whereby a protein target of interest is modelled and small molecules docked to it to predict binding and therefore enzymatic inhibition potential. Ligand-based virtual screening aims to use knowledge of small molecule binders and inhibitors to predict similarly active small molecules. We propose a third approach; phenotype-basedvirtual screening whereby molecules are assessed based on their observed similarityto phenotype inducing biologically active molecules.The successful candidate for this project will apply machine learning techniques to an existing, large dataset of high content imaging data (Carragher lab, currently proprietary, unpublished), capturing the phenotypic response of eight cell lines to 14,000 diverse small molecules. Cellular phenotypic response is encoded in 340 features per cell [2]. Machine learning techniques will be taught to associate phenotypic responses with small molecule features, Trained AI software will be able to associate features present in a battery of available molecular fingerprints and associate these with a phenotypic response [3]. The ultimate utility in this work lays in the application of these predictors to new, unseen molecules, in order to predict molecules capable of eliciting a desired, predefined, exploitable, therapeutically relevant, efficacious response.