Informatics
The reduced graph is entered into the encoder and the
decoder generates a molecule
scan past literature to see if similar ideas have already been tried, helping focus their direction of research. With the target identified, AI can be used for
molecular generation from scratch, identifying proposed new molecules and performing virtual test cycles. While AI is some way from creating drugs without human guidance, this in invaluable in bringing focus to the idea generation process. Efficacy is only one part of the puzzle; molecules
also need a whole host of other properties predic- tion – such as absorption, distribution, metabolism, elimination and toxicity (ADMET). For identified candidates, AI can perform in silico property prediction, allowing poor candidates to be eliminated early, and increasing throughput of good quality leads. While in silico modelling is nothing new, it is getting better with more data and better algorithms. Neural networks can also be used for predicting
retrosynthesis routes, assessing the synthesisability of a candidate, and so helping understand how easy the drug is to make. This helps improve planning, and eliminates avenues that will not scale viably. Beyond drug discovery, it is worth noting that AI
is being used across the drug lifecycle, from opti- mising production processes, to gathering clinical data, to assessing variations in populations that could affect response rates. Life sciences companies
30
are increasingly breaking down silos and viewing the drug development process more holistically. Data and insights from across the value chain could be used to inform the discovery process (and dis- covery data will be useful further down the line). A holistic approach to technology, data and people will ensure benefits are felt as widely as possible.
How is AI different to current data-led drug discovery approaches? Using data to improve predictions and automation is not new; what AI brings is an ability to deal with new levels of scale and complexity in data. Statistical methods work within the constraints of fixed assumptions based on scientific understand- ing, but AI can be given a broad brief (eg find a molecule that meets this criteria). AI represents the coming together of a number
of trends. Various machine learning techniques, a subset of AI, have been used in R&D for a while, but advances in algorithms and new, more sophis- ticated tools, such as such as generative adversarial networks (GANs), are adding the ability to tackle more complex and ambitious tasks. These advances have also been made possible thanks to the explosion in data available to process, and the exponential increases in compute power. Some of this benefit is simply about bigger and better, or more with less. Some is about replicating
Drug Discovery World Fall 2019
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