Informatics
How ARTIFICIAL INTELLIGENCE is transforming drug design
AI could create a streamlined, automated approach to drug discovery, trawling vast datasets to identify targets, find candidate molecules and predict synthesis routes. Getting there will require significant vision, but we are already seeing exciting examples of AI helping direct research and reduce discovery times. We discuss the short and long term potential of AI in drug discovery.
F
ew topics attract excitement and caution like AI. Despite huge investments, pharma is an industry wary of hype, and is doubly cau-
tious about handing over their highly-regimented R&D processes to hard-to-understand algorithms. Scepticism is healthy, but AI is coming. Exactly
when will depend on how enthusiastically it is embraced, but once one company gets it right, and proves it, the rest will quickly follow. But whether a full industry transformation happens in two years or 10, organisations need to understand AI, and how it can benefit their business, so they can make informed decisions in the short and long term.
How will AI transform pharma? At the heart of Pharma R&D is the development of new drug molecules, which are effective against a particular biological target involved in disease. This involves huge numbers of experiments, pre- dictive models and expertise, applied across several rounds of optimisation, each with modifications to the best set of potential molecules. Long term, AI offers the hope of a streamlined
and automated approach across these various stages. A future AI may hold within its databases the sum of all knowledge about biology, genes and chemical interactions. It will be able to identify new targets and find candidate molecules for a par- ticular target in silico from vast libraries, and
Drug Discovery World Fall 2019
develop and refine molecules to home in on the best ones. It will be able to specify how to synthe- size the candidate molecules, gather test data and refine further. That dream is some way off, but AI is already
automating many parts of the drug discovery pro- cess. Analytics and statistical models have long been used to reduce trial and error in drug discov- ery. AI has the potential to remove much more, and home in on better answers much quicker than is currently possible. Even short term, AI could con- ceivably shave a year off the development of many drugs, which would be worth billions. To benefit from AI short term, companies are
looking at how it can deliver across different parts of the discovery process: some in a piecemeal way, some with a view to building towards complete AI driven digitalisation as the technology develops.
Near-term AI applications Target identification and validation can be achieved using AI to identify biological entities to target. For example, AI and natural language pro- cessing (NLP) can be used to scan vast tomes of medical literature and genetic datasets to look for clues about gene-disease associations, to identify new targets. Previous literature may also contain clues as to how to tackle this, eg when research made interesting discoveries but did not progress them. A company progressing an early idea can
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By Dr Mark Roberts and Dr Sam Genway
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