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Does your model weigh the same as a duck?

Ajay N Jain1, Ann E Cleves

  • 1Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94158-9001, USA. ajain@jainlab.org

Journal of Computer-Aided Molecular Design
|December 22, 2011
PubMed
Summary
This summary is machine-generated.

Computer-aided drug design (CADD) shows successes, but logical fallacies like correlation imply causation and confirmation bias undermine its methodology and validation. This study identifies and provides examples to avoid these issues in CADD research.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development

Background:

  • Computer-aided drug design (CADD) is a significant field with established successes in studying small molecule interactions.
  • However, critical logical fallacies persist in CADD methodology and validation arguments.
  • Common fallacies include 'cum hoc ergo propter hoc' (correlation implies causation) and confirmation bias.

Purpose of the Study:

  • To critically examine the logical fallacies prevalent in CADD research.
  • To discuss the impact of these fallacies on areas such as off-target prediction, QSAR, molecular similarity, and docking.
  • To provide examples of research that successfully avoids these logical pitfalls.

Main Methods:

  • Analysis of logical fallacies in the context of established CADD techniques.
  • Illustrative examples demonstrating the application of 'cum hoc ergo propter hoc' and confirmation bias.
  • Presentation of alternative approaches and examples that mitigate these logical errors.

Main Results:

  • Identified 'cum hoc ergo propter hoc' and confirmation bias as pervasive issues in CADD.
  • Demonstrated how these fallacies can lead to flawed conclusions in predictive modeling and validation.
  • Provided concrete examples of CADD studies that successfully circumvent these logical errors.

Conclusions:

  • Rigorous logical reasoning and validation are crucial for advancing CADD.
  • Awareness and avoidance of logical fallacies are essential for reliable computational drug design.
  • Future CADD research should prioritize methods that are free from these cognitive biases and logical errors.