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LabAutomation


A 3D image acquired on the Opera Phenix using a 63x water immersion objective. The sample are cysts made up of MDCK cells, labelled with Draq5 it shows both Phenotypic cells and power of AI driven image analysis


ing and has been applied to microscopy image analysis13,14. Other methods of deep learning have been used in different contexts for image analysis: histopathology images can be analysed to deter- mine phenotype associated with gene expression15. Such work would have previously required manual inspection of the slides. Finally, morphological classifiers of cell phenotypes upon small molecule exposure have recently been analysed with CNNs16. Another successful area of application for deep


learning is genomics. Large amounts of data in the field allow leverage of machine learning models to identify biomarkers associated with phenotype17 or level of gene expression18. The amount of data gen- erated from whole genome sequencing is massive and collaborations between academic and tech firms to capitalise on their analysis have increased rapidly, for example in that between Google and the Broad Institute to implement the Broad’s open-source genomics toolkit on Google’s cloud servers19. The same methods can be used to search for


therapeutics with the potential to silence genes or inhibit specific gene activity. For example, artificial


Drug DiscoveryWorld Summer 2019


neural networks (ANN) have been used to predict siRNA activity on predefined target sequences20. Another interesting deep learning application


covers the design of new peptides with high bioac- tivity. Combinatorial peptide libraries challenge creators through the sheer numbers of generated compounds. It is then crucial to select the peptides with the highest activity for synthesis. Machine learning provided a method to predict that activity and sort the selected peptides21. Other machine learning methods provided a way to predict pro- tein biding site for small molecules22 or prediction of protein expression and solubility of a large dataset to optimise high-throughput experi- ments23. These projects would not have been possible


without the use of both automation and machine learning. Specific models and methods from machine learning are keys to answer specific scien- tific questions. The diversity of methods is key to the diversity of biological questions. It is why the industry has been turning to technology compa- nies for models such as GAN (generative adversar- ial networks)24, for customised hardware25 or for


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