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LabAutomation


AUTOMATING


AUTOMATION how close are we to Artificial Intelligence impact?


In terms of consistency, repeatability, known errors and sheer volume, there exists perhaps no better collection of data for computer learning than that emerging from automated processes.Many common lab procedures now run in parallel,miniaturised experiments – DNA synthesis, target screening, organoid culture, genetic analysis, organic reactions, safety assays – which are poised for extensive curation and algorithm development over the next 10 years.This article briefly outlines each area and offers opinions about how close we are to having artificial intelligence (AI), deep learning (DL) or machine learning (ML) influence each scientific domain.


By Dr MichaelA. Tarsellia,DrYohann


Potier and DrAlan E. Fletcher


T


he past 10 years have seen an amazing change in the miniaturisation, cost reduc- tion, high-fidelity and data acquisition of


modern instrumentation; the surge of robotic-con- trolled processes enabling DNA synthesis, genome editing, screening, plating and cell culture have led to a data explosion. In the nineties and early 2000s, the introduction of automation and high- throughput screening transformed the way in which drug discovery research was performed, leading to a rise in the number of compounds test- ed against a target of interest and a significant amount of investment in the quest to produce the ultimate screening factory. Massive repetition led to consistency – lower error (greater precision with higher number of experiments), better fidelity and the ability to quickly generate enough data to run in silico or ‘virtual’ experiments. This boon has generated yet another problem, that of ‘Big Data’1 where sophisticated algorithms must infer patterns


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from large warehouses of data to distil wisdom from gathered information. Now, as many researchers struggle with the ever-increasing com- plexity of drug development and the rise of person- alised medicine approaches, the increasing use of Artificial Intelligence (AI)/Deep Learning (DL) pre- sents one of the most promising and transforma- tive opportunities for the life sciences and medical industries2. The emergence of AI/DL in drug discovery pro-


vides many advances over traditional techniques in genomics, image analysis and medical diagnostics3 and is one of the reasons that pharmaceutical com- panies such as Merck, Sanofi, AZ and Takeda are placing big bets on the ability of AI to deliver improvements in quality, clinical success rates and reduced costs4. This short perspective will show the reader how


recent revolutions chemical and biological automa- tion produce enough data and learning to build a


Drug DiscoveryWorld Summer 2019


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