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Related Experiment Video

Updated: Feb 8, 2026

Blind Endotracheal Intubation in Neonatal Rabbits

Blind Endotracheal Intubation in Neonatal Rabbits

Published on: February 26, 2021

Blind Challenges Let Us See the Path Forward for Predictive Models.

John D Chodera¹, W Patrick Walters², Sriram Kosuri³

¹Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States.

Journal of Chemical Information and Modeling

|February 6, 2026

Summary

This summary is machine-generated.

Artificial intelligence and machine learning models promise to accelerate drug discovery. Blind challenges are essential for accurately assessing predictive performance and overcoming accuracy barriers in computational drug design.

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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.