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Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

Mean free path and Mean free time

Mean free path and Mean free time

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Path Between Thermodynamics States

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VSEPR Theory for Determination of Electron Pair Geometries

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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.