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LabAutomation


collaboration with experts in machine learning algorithms coupled with large-scale cloud capabil- ities26. Entire companies’ business models are based on machine learning methods for drug dis- covery: the unique combination of cloud technol- ogy, automation and machine learning provides opportunities in life science at a scale we could not manage before (vide supra).


Instrumentation – image analysis, IoT, virtual help Prognosis: imaging and voice interaction prototypes. Wider adoption pending The concept of a ‘Robotic Scientist’ is not new and was first conceptualised in 200427 as a combina- tion of computational methods, automated instru- ments linked to complex laboratory robotic sys- tems and the need for AI and machine leaning to test and iterate on a hypothesis in real time. Cellular imaging provides an ideal opportunity


to showcase the power of AI. High content screen- ing (HCS) or cellular imaging and analysis is today widely used across many parts of the drug discov- ery process, driven by the need to gain greater understanding of the phenotypic nature of interac- tions between potential new drug candidates and the cells found in the human body. The ability to predict and visualise potential unwanted cellular interactions increases the potential of success when a candidate enters the clinical phase of testing, however the vast quantity of information provided by even the most basic of HCS platforms opens itself to the need for more intelligent forms of data analysis. AI techniques provide the ability to pro- vide enhanced segmentation and even to identify specific organelle structures in the absence of seg- mentation, precluding the need for cell labelling3. In addition, so called image-based cell profiling


strategies28 provide a path to high-throughput quantitation of phenotypic differences in cell pop- ulations through the collection and analysis of hundreds of morphological changes caused by prior treatment with a chemical or biological agent. This analysis approach provides a path to identification of novel targets or mechanisms of actions for prospective new drugs. Given the number of sensors and facile commu-


nication among various lab instrumentation, a growing movement seeks to capitalise in two dis- tinct but complementary ways: building ‘internet of things’ networks to alert scientists29, or two- way communication using robotic lab assistants powered by natural language processing and virtu- al models30. In summary, automation itself has provided the


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high-quality, high volume data required to train neural networks and recognise patterns. As new tasks are automated for both chemistry and biolog- ical processes, the instruments also gain sensor data: location, position, temperature, torque. Soon, it will not be even that a gene is properly functioning or a reaction works, but more subtle ways of discerning how the robot or instrumenta- tion runs the assay that will be the rate-limiting step to scientific innovation.


Authors’ note This piece was commissioned in part to celebrate the inaugural


‘AI


in Process Automation


Symposium’ which will be held in Boston, MA, USA in October 2019. The Society for Laboratory Automation and Screening (SLAS), of which all authors are volunteer leaders or staff, appreciates Drug Discovery World’s kind invitation and facili- tation to produce this perspective piece. DDW


Dr Michael Tarselli trained as a synthetic organic chemist and worked in CROs, start-ups and multi- ple pharma companies as a chemist, before leading a team in chemical information systems develop- ment at Novartis. Since 2018 Mike has served as the Scientific Director for SLAS.


Dr Yohann Potier trained as a computational chemist and spent time in business analysis and informatics before landing at Novartis. He leads a team creating new solutions to support bench sci- entists across the biological and bioinformatics community. Yohann is also involved in local con- sortia such as Pistoia Alliance.


Dr Alan Fletcher trained as a pharmacologist, then spent a decade running HTS and robotics forMSD in the United Kingdom. On the business side, he has served as a Director and VP GM for Life Sciences at two technology firms. He is the 2019 President of the SLAS Board of Directors.


Drug DiscoveryWorld Summer 2019


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