Researchers at the University of Bonn, led by Prof. Dr. Jürgen Bajorath, have discovered that ‘black box’ AIs in pharmaceutical research rely on recalling existing data rather than learning new chemical interactions, challenging previous assumptions. The study focuses on Graph Neural Networks (GNNs) and suggests improved training techniques could enhance their performance. Prof. Bajorath advocates for cautious treatment of assumptions about AI.
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Uncovering the Inner Workings of AI in Pharmaceutical Research
Scientists at the University of Bonn, led by Prof. Dr. Jürgen Bajorath, have revealed that AI models in drug discovery predominantly depend on recalling existing data rather than learning new chemical interactions. This challenges previous assumptions about how AI makes predictions in this field.
Practical Implications:
Practical solutions and value:
- AI-assisted drug discovery has seen major breakthroughs, including the analysis of millions of compounds for potential therapeutic effects, drugs showing promise in slowing aging, and AI-generated proteins showing excellent binding strength.
- The study emphasizes the importance of cautious treatment of assumptions about AI’s capabilities and advocates for the development of methods to elucidate AI models, especially in the realm of “Explainable AI.”
The “Clever Hans Effect” in AI
Researchers have likened the phenomenon to the “Clever Hans effect,” where a horse seems to perform arithmetic by interpreting subtle cues from its handler rather than actually understanding mathematics. Similarly, the AI’s predictions are more about recalling known data than understanding complex chemical interactions.
Practical Implications:
Practical solutions and value:
- The findings suggest that simpler methods might be equally effective in some cases, and improved training techniques could enhance the performance of AI models.
- Prof. Bajorath’s team is working on developing methods to further elucidate AI models, especially in the realm of “Explainable AI,” aimed at making AI’s decision-making processes transparent and understandable.
Practical Advice for AI Implementation
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Practical Solutions:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
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Practical Solutions and Value:
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