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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction.

Davor Oršolić1, Tomislav Šmuc1

  • 1Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb 10000, Croatia.

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Summary
This summary is machine-generated.

This study introduces a new machine learning method for drug discovery, enhancing confidence in binding affinity predictions. The approach improves accuracy by considering local compound-target interactions.

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Area of Science:

  • Computational chemistry
  • Machine learning
  • Drug discovery

Background:

  • Machine learning models are crucial for drug discovery, but often lack rigorous confidence assessments for predictions.
  • Predicting compound-target binding affinity is a key task in cheminformatics and computational drug design.
  • Existing conformal prediction methods offer confidence estimates but can be improved for complex interaction data.

Purpose of the Study:

  • To extend the inductive conformal prediction framework for improved compound-target binding affinity prediction.
  • To develop a novel approach that integrates applicability domain concepts with conformal prediction for more reliable confidence estimates.
  • To provide more informative prediction regions for individual compound-target interactions.

Main Methods:

  • Developed a novel inductive conformal prediction framework utilizing dynamically defined, pair-specific calibration sets.
  • Incorporated the concept of a compound-target neighborhood to leverage local model properties for predictions.
  • Combined the applicability domain paradigm with conformal prediction for enhanced confidence assessment.

Main Results:

  • The novel approach demonstrated superior confidence assessment compared to state-of-the-art conformal prediction methods.
  • The framework produced valid and more informative prediction regions in complex binding affinity prediction scenarios.
  • Benchmarking on public datasets and realistic use-case scenarios validated the effectiveness of the proposed method.

Conclusions:

  • The proposed method offers a more rigorous and informative confidence assessment for binding affinity predictions in drug discovery.
  • Dynamically defined calibration sets and neighborhood-based analysis improve the reliability of machine learning models in cheminformatics.
  • This work advances the application of conformal prediction for robust compound-target interaction modeling.