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Prospective value of benchmark-excelling AI models

Ascertain which artificial intelligence models that excel on retrospective benchmarks also have prospective value in small-molecule drug discovery and determine the extent of AI’s real-world impact across discovery tasks.

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Background

The authors emphasize that while AI is having a significant impact on drug discovery, there is uncertainty about how this impact translates to prospective applications. They argue that many AI-for-Science projects are guided by problem-misaligned benchmarks and that reproducibility issues further cloud evaluation, making it unclear which models that perform well retrospectively are truly valuable prospectively.

References

Nevertheless, it is often unclear to which extent this is the case and which of the AI models that excel in benchmarks also have prospective value.

— Data-centric challenges with the application and adoption of artificial intelligence for drug discovery (2407.05150 - Ghislat et al., 6 Jul 2024) in Section 5.1 Researchers with expertise in an AI topic