Blind Challenges Let Us See the Path Forward for Predictive Models

Area of Science:

• Computational chemistry
• Drug discovery informatics
• Artificial intelligence in medicine

Background:

• AI/ML models are rapidly advancing drug discovery, but their predictive accuracy is often overstated.
• Diverse molecular representations are used for on-target (structural) and off-target/ADMET (implicit) predictions.
• Existing retrospective benchmarks may not accurately reflect real-world predictive success.

Purpose of the Study:

• To address the need for realistic evaluation of AI/ML model performance in drug discovery.
• To highlight the limitations of current benchmarking methods for molecular property prediction.
• To emphasize the importance of prospective, standardized comparisons.

Main Methods:

• Discusses the role of retrospective benchmarks and their limitations.
• Highlights the significance of blind challenges (e.g., OpenADMET × ASAP × PolarisHub Challenge).
• Emphasizes the need for standardized, prospective comparisons of predictive models.

Main Results:

• Retrospective benchmarks can be misleading regarding model performance.
• Blind challenges offer a more realistic assessment of predictive capabilities.
• Community-led initiatives and open data are crucial for progress.

Conclusions:

• Standardized, prospective evaluations are vital for validating AI/ML models in drug discovery.
• Blind challenges are key to identifying and overcoming accuracy barriers.
• Continued investment in data and community challenges will accelerate AI-driven drug discovery.