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Using Predicted Bioactivity Profiles to Improve Predictive Modeling.

Ulf Norinder1,2,3, Ola Spjuth2,4, Fredrik Svensson5

  • 1Department of Computer and Systems Sciences, Stockholm University, Box 7003, SE-164 07 Kista, Sweden.

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Summary
This summary is machine-generated.

Generating predicted bioactivity profiles using conformal prediction enhances predictive modeling accuracy in early drug development. Including p-values from conformal prediction as bioactivity profiles offers the most significant efficiency improvements.

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Area of Science:

  • Computational chemistry
  • cheminformatics
  • drug discovery

Background:

  • Predictive modeling is crucial for early drug development.
  • Integrating data from multiple domains can improve predictive model performance.
  • Data aggregation often results in incomplete matrices, posing modeling challenges.

Purpose of the Study:

  • To explore the use of predicted bioactivity profiles as additional features to enhance predictive modeling.
  • To present a robust framework using conformal prediction for generating and evaluating these profiles.
  • To assess the impact of different approaches for generating predicted profiles on prediction accuracy.

Main Methods:

  • Utilized conformal prediction, a confidence-based predictor, to generate bioactivity profiles.
  • Applied several methods to generate predicted profiles across 16 datasets.
  • Evaluated the impact of these profiles on prediction accuracy for biological endpoints.

Main Results:

  • Generating predicted bioactivity profiles improved prediction accuracy for biological endpoints.
  • The framework using conformal prediction proved robust for profile calculation and impact evaluation.
  • Including p-values derived from conformal prediction as bioactivity profiles yielded the greatest efficiency gains.

Conclusions:

  • Predicted bioactivity profiles, particularly those derived from conformal prediction (p-values), are valuable features for improving early drug development predictive models.
  • Conformal prediction offers a reliable method for generating informative bioactivity profiles.
  • This approach addresses data incompleteness issues and enhances the efficiency of predictive modeling.