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For chemists, the AI revolution has yet to happen

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Summary

Artificial Intelligence (AI) has not yet revolutionized the field of chemistry due to the lack of data available to feed AI systems. Chemists are attempting to create or access more and better data by setting up systems that pull data out of published research papers and existing databases, and by automating laboratory systems. To make AI tools more effective, data must be recorded in agreed and consistent formats, including information on negative outcomes, such as reaction conditions that don’t produce desired substances. By taking these steps to collect and share data, AI can meet the expectations of chemists and avoid becoming simply hype.

Q&As

What potential risks of artificial intelligence (AI) has prompted a prominent AI figure to resign from Google?
The potential risks of artificial intelligence (AI) that has prompted a prominent AI figure to resign from Google include potential risks to society and human well-being.

What is the problem preventing chemists from harnessing the full potential of AI in their field?
The problem preventing chemists from harnessing the full potential of AI in their field is the lack of data available to feed hungry AI systems.

How much data is needed to apply a generalist generative-AI system to chemistry?
To apply a generalist generative-AI system to chemistry, hundreds of thousands — or possibly even millions — of data points would be needed.

What is AlphaFold and why is it a good example of AI power?
AlphaFold is an AI protein-structure-prediction tool that was trained on the information in the Protein Data Bank, which contains more than 200,000 structures. It provides an excellent example of the power AI systems can have when furnished with sufficient high-quality data.

What steps need to be taken to collect and share data to allow AI to reach its full potential in chemistry?
To allow AI to reach its full potential in chemistry, steps need to be taken to collect and share data. This includes setting up systems that pull data out of published research papers and existing databases, automating laboratory systems, and ensuring data is recorded in agreed and consistent formats. Additionally, data should include negative outcomes, such as reaction conditions that don’t produce desired substances.

AI Comments

👍 This article provides a comprehensive overview of the potential of AI in the field of chemistry and how it can revolutionize the industry.

👎 This article does not provide any concrete solutions to the lack of data available to feed AI systems in the field of chemistry.

AI Discussion

Me: It's about how artificial intelligence (AI) hasn't revolutionized chemistry yet, and how it could if we had more data and access to it. It talks about how current AI tools need comprehensive knowledge of chemical structures to make accurate predictions, and how there needs to be more data available for AI systems to use. It also mentions the need for agreed and consistent data formats, and the need for open data.

Friend: That's really interesting. Do you think AI will eventually revolutionize chemistry?

Me: I think it's definitely possible! AI has already made some progress in the field, and with more data and better access to it, it could unlock a lot of potential. I think it's important for researchers to focus on data accessibility and work together to create comprehensive training data sets so that AI can reach its full potential in chemistry.

Action items

Research existing AI tools for chemistry and evaluate their potential for use in research.
Explore ways to automate laboratory systems to generate and measure compounds to test AI model outputs.
Investigate ways to make data more accessible, such as setting up systems to pull data out of published research papers and existing databases.

Technical terms

AI (Artificial Intelligence): AI is a type of computer technology that is designed to simulate human intelligence and behavior.
Neural Networks: Neural networks are a type of artificial intelligence system that uses interconnected layers of neurons to process data and make decisions.
Generative AI: Generative AI is a type of artificial intelligence system that is used to generate new data from existing data.
Retrosynthesis: Retrosynthesis is a type of chemical reaction in which a molecule is broken down into its component parts in order to determine the best starting materials and sequence of reaction steps to make it.
Inverse Design: Inverse design is a process in which desired physical properties are identified and then substances that have these properties are identified.
Graph Representation: Graph representation is a type of data structure in which nodes are connected by edges. In chemistry, chemical bonds connect atoms, just as edges connect nodes in graphs.