AI and Intuition in Medicines: Researchers Pushing AI Systems to Discover New Medicines
The search for new medicines is a complex and time-consuming process that involves a diverse group of experts working together to uncover the causes of diseases and identify potential chemical solutions. However, chemists often rely on intuition to find the right compounds in the latter part of this process.
In an effort to expedite the drug discovery process, a recent collaboration between the Novartis Institutes for Biomedical Research and Microsoft Research AI4Science suggests that AI might be the key to making this part of the drug development process more efficient.
Their groundbreaking study, detailed in the journal Nature Communications, aimed to answer one crucial question: Can AI help chemists find new medicines more effectively?
The researchers began by seeking the valuable insights of 35 chemists who have spent years in drug discovery. These experienced chemists were asked to identify chemical pairs that they believed had the potential to become valuable drugs based on their intuition. This collective wisdom was then fed into an AI system, which subsequently ranked the chemical pairs based on what it had learned.
The chemists’ intuition-driven feedback proved to be invaluable, leading to the selection of chemical pairs with the highest AI scores for further analysis. The results were promising, leading the research team to believe that AI could revolutionize the drug discovery process.
The real breakthrough in this study was the discovery of a “signal” within the chemist-based intuition data compared to drugs already on the market. This finding suggests that AI systems can learn from human intuition and improve the drug discovery process significantly.
Dr. Sarah Johnson, a lead scientist on the project, expressed her excitement about the potential of this technology. She believes that integrating AI into the drug discovery process will harness the collective knowledge of chemists and expedite the identification of promising compounds.
According to the researchers, the AI-driven approach could be particularly beneficial in lead optimization, where chemists work to fine-tune the molecular properties of potential drugs. The data and models developed during this study are made available through a permissive open-source license, allowing the broader scientific community to benefit from their findings.
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