Key Takeaways
Accelerating Failures with AI:
AI and machine learning are identifying likely failures earlier in the pipeline. Tools like adaptive trial designs and predictive analytics can cut timelines significantly, optimizing resources and reducing late-stage disappointments.
Prioritize Learning Alongside Failure:
Companies like Recursion use granular data tracking and model testing to learn fast from failures. This approach avoids repeated mistakes and helps fine-tune strategies for better success rates.
Strategic Portfolio Management:
At Nimbus, decisive program halts enable capital and focus to shift toward high-probability winners. Transparent communication about portfolio strategies fosters alignment among scientists, partners, and investors.
The stats are known by now: 90% of drug candidates fail during the development process
According to Kailash Swarna, managing director and global clinical lead at Accenture Life Sciences, AI is helping in this respect in a number of ways
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