Traditional pre-clinical scientists urgently need practical & effective ways to take advantage of key computational technologies – i.e. bioinformatics, machine learning, AI (classical, causal, generative, etc) and everything in between. The mainstream adoption of these technologies will accelerate drug discovery, while lowering costs and risks in the UK Life Sciences industry.

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As we look forward, there is a holistic vision for the Lab of the Future, which integrates advanced lab techniques, automation, and data together with effective data science & AI techniques.  This program focuses on the latter, i.e. how data science / AI can help lab scientists getting to better medicines faster and cheaper.

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Example questions for us to look at:

• How could data science / AI enhance the lab scientists workflow?

• What does the drug discovery workflow (idea through to in vitro) actually look like?

• How does a biotech researcher know where possible process improvements are?

• Where computational methods ("AI") are helpful and where are they not?  Why?

• Where are key scientific decisions made that need to stay manual?

• What are the possibilities for the future across the map?  Including learning from downstream (e.g. RWD) datasets?

 

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Join this community action group to demystify what goes on and what could go on.  Let's democratize to scientists everywhere what the potential best practices could be and where the pitfalls lie!

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Together, with a little bit of help from everybody, we will build a map of what goes on and what could go on - including manual steps, automation, AI, data flows etc.

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Biotechs, researchers and vendors are all welcome to participate. 

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Pre-reading to get us going:

https://www.nature.com/articles/nrd.2017.217 

https://www.nature.com/articles/nrd1523

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Next steps to get going:

1. Develop a quick strawman version of the answer, so we have something to build on.

2. Reach out to the vendor community for adds & refinement.

3. Push out a short questionnaire to biotechs for "demand side" thinking / requirements / truths.

4. Integrate the above and then work with the Action Group to begin building a credible view.