There is a lot of hype and debate on AI in drug discovery and laboratory research, but where does it fit in your research? Hear from our founders on its near- and long-term potential to impact the workflow for photocatalysis and similar reactions.
AI is already impacting drug development and the pharmaceutical industry and has the potential to be transformative in the next few years.
AI in Chemistry & Drug Discovery
AI has the potential to bring a lot of value to chemistry—it could synthesize millions of chemical materials and find a better way to make drugs. Adoption has been slower among chemists than in other fields so far.
Because AI can process and analyze large and complex data sets, it will be able to assist researchers in testing parameters. For example, in spectroscopy, AI could highlight chemicals, run reactions, check the chemistry, and interpret results.
AI is expected to have a vast economic impact on all areas of the life sciences sector, from early-stage research to bringing a drug to market and managing it.
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Challenges
Putting AI tools in place for drug discovery and development is all new territory and has some challenges, including:
- Staff resistance and uncertainty
- Intellectual property and data privacy issues
- Regulatory compliance
- Data security, including cyber security threats
- Flawed data models
Founder and President Iain MacMillan shares, "As a physicist, I see a lot of potential. We may be able to synthesize millions of chemical materials to find a better way to make drugs. Most chemists I know think that's a stretch. At some point AI will find its spot in chemistry, though it's not there yet. There's no substitute for original thought, but in data analysis and pattern recognition? For sure."
Acceled's photoreactors, m2 and cLight, currently offer software in which researchers can record results and parameters in formats that may lend themselves to AI data analysis.
