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.
The past 10 years have seen an amazing change in the miniaturisation, cost reduction, high-fidelity and data acquisition of modern instrumentation; the surge of robotic-controlled 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 highthroughput screening transformed the way in which drug discovery research was performed, leading to a rise in the number of compounds tested 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 from large warehouses of data to distil wisdom from gathered information. Now, as many researchers struggle with the ever-increasing complexity of drug development and the rise of personalised medicine approaches, the increasing use of Artificial Intelligence (AI)/Deep Learning (DL) presents one of the most promising and transformative opportunities for the life sciences and medical industries (2).
The emergence of AI/DL in drug discovery provides many advances over traditional techniques in genomics, image analysis and medical diagnostics (3) and is one of the reasons that pharmaceutical companies 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 costs (4).
This short perspective will show the reader how recent revolutions chemical and biological automation produce enough data and learning to build a deep learning model pipeline, making science faster, more efficient and more accurate....
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