Closing the data loop in AI-driven drug discovery
The pharmaceutical industry faces challenges in drug discovery due to high costs and long timelines, with AI being used to optimize compound identification and reduce development time. While AI can enhance hit identification and compound quality, it lacks the ability to reliably predict kinetics or developability, requiring lab validation. The demand for more data-rich technologies is growing, and current AI models struggle with data quality and bias, especially due to limited datasets and publication bias. Integrating lab systems is essential for AI to function effectively, and advancements in AI-driven drug discovery aim to accelerate breakthroughs while balancing cost and risk.
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