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Related Experiment Video

Updated: Jul 20, 2025

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
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Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values.

Alec Lamens1, Jürgen Bajorath1

  • 1Department of Life Science Informatics, B-IT, LIMES Program, Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany.

Molecules (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a test system for understanding machine learning (ML) predictions in drug discovery. It uses explainable artificial intelligence (XAI) methods to provide chemically intuitive rationales for compound activity predictions.

Keywords:
SHAP valuescounterfactualsdual-target compoundsexplainable artificial intelligencemachine learningmulticlass activity prediction modelssingle-target compounds

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Area of Science:

  • Computational chemistry
  • Machine learning
  • Artificial intelligence

Background:

  • Machine learning (ML) models in pharmaceutical research often function as "black boxes", hindering their adoption for guiding experimental work.
  • Explainable ML (XML), a subset of explainable artificial intelligence (XAI), is gaining traction to improve the interpretability of ML predictions.

Purpose of the Study:

  • To develop and validate a test system for rationalizing multiclass compound activity prediction models.
  • To enhance the understanding and trust in ML models within pharmaceutical research.

Main Methods:

  • Integration of two XAI approaches: counterfactuals (CFs) and Shapley additive explanations (SHAP) for feature relevance analysis.
  • Development of a test system to analyze multiclass compound activity prediction models.
  • Identification of small compound modifications that alter prediction outcomes.

Main Results:

  • The developed system successfully rationalizes multiclass compound activity prediction models.
  • Counterfactuals and SHAP analyses identified specific compound modifications that invert predicted activities.
  • Feature mapping in conjunction with CFs and SHAP provided chemically intuitive explanations for model predictions.

Conclusions:

  • The combined use of CFs and SHAP offers a powerful method for explaining ML predictions in compound activity assessment.
  • This approach facilitates a deeper chemical understanding of model decisions, promoting the use of ML in drug discovery.
  • The system provides actionable insights for rationalizing compound modifications and guiding experimental design.