One risk for chemistry is that in many small

firms and academic labs, data is still recorded in paper lab notebooks, which slows the amount of AI adoption to early discovery and more risky reactions. Automation provides consistent data templates and inputs for algorithms, which means a gap to overcome would be to decide which pro- cesses are automatable. Perhaps due to the large data warehouses and mandated electronic storage of experimental records and instrument data, prac- tical innovations in this space from industry have outpaced academia. Another cautionary tale has come from the use

of sophisticated AI systems such as IBM’s Watson, famous previously for accomplishments in beating human competitors in Jeopardy and assisting doc- tors with medical literature gathering. However, IBM announced in early 2019 that it had halted drug discovery applications of its system. This may also be an indication of the maturity of AI in this field; IBM’s exit was not seen as an algorithmic issue, but rather one of insufficient high-quality data on which to base drug predictions11. To be certain, as observed in Ian Davies’ seminal Nature perspective12, we face many challenges to wider adoption of AI in chemistry automation – consis- tent data formats, standard automation sets, cul- tural shifts in reporting and practice.

Discovery biology – from DNA to screening Prognosis: gaining traction Various methods of AI have been successfully

Hexagonal blocks of circuit board and mathematical formulae

Illustration courtesy of Ikon

Images/Ben Miners/Science Photo Library

applied in drug discovery outside the small molecule world. AI, and more specifically deep learning, are useful on several levels for life science and biologists in particular: AI improves the qual- ity of the obtained insight and allows scientists to handle the large volume and speed of data coming from automation that would otherwise require ‘superhuman’ effort from a single bench scientist. Biological deep learning allows scaling-up of

effort and expedites traditionally manual steps. These successes depend heavily on several factors: the amount of data, the type of data to analyse and the methods used for the analysis. Unlike most bench chemistry, diversity in biology data is the main hurdle. Another obstacle, machine learning, requires large amounts of data across the range of expected measurements of a given experiment, yet also a large amount of data necessary for the train- ing, validation, testing and finally execution of the machine learning method. Though a general ‘biology agent’ does not yet

exist, deep learning methods have been applied to more narrow questions with success. One application? Phenotypic screening. Just like

image recognition of cats, we can leverage similar algorithms for microscopy image analysis. Image- based screening is particularly suitable for high- throughput cell biology where microscopy images provide scientists with means to determine differ- ent types of cells in a sample, counting the cell or if proteins are expressed or to identify cell anatomy. One deep learning model, the convolutional neural network (CNN), was developed for image process-


Drug DiscoveryWorld Summer 2019

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