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Big Picture Medicine
#122 Why AI Drug Discovery Is So Hard — Dr Imran Haque (Recursion)
Sep 4, 2023
Dr Imran Haque, SVP of AI and Digital Sciences at Recursion Pharmaceuticals, discusses why AI drug discovery is challenging, the need for better understanding of drug interactions, the trade-offs in translational models, advancements in computational modeling and data acquisition in biology, and the value of being a generalist in the field.
40:32
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Quick takeaways
- Decoding biology and understanding its complexities hinder AI drug discovery progress.
- AI drug discovery relies on data collection, computational modeling, and continuous experimentation to decode biology and accelerate drug discovery.
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The Challenges of AI Drug Discovery
AI drug discovery has garnered significant attention and investment, but no drug discovered using AI has made it to market yet. The complexities and difficulties of decoding biology are the major factors hindering progress in this field. The lack of deep understanding about how biology works and the limitations of linear hypotheses about disease contribute to the challenge. The approach taken by companies like Recursion involves collecting extensive data about biology and chemistry, leveraging deep computation to build models, and continuously testing predictions through experiments. The goal is to decode biology and accelerate the drug discovery process for new diseases and specific patient populations.
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