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.
Few topics attract excitement and caution like AI. Despite huge investments, pharma is an industry wary of hype, and is doubly cautious 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, predictive 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 particular target in silico from vast libraries, and develop and refine molecules to home in on the best ones. It will be able to specify how to synthesize 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 process. Analytics and statistical models have long been used to reduce trial and error in drug discovery. 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 conceivably 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....
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