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Benchmarking Molecular Feature Attribution Methods with Activity Cliffs.

José Jiménez-Luna1,2, Miha Skalic2, Nils Weskamp2

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Feature attribution methods highlight important molecular features for drug design. A new benchmark shows classical models outperform graph neural networks for this task, aiding explainable AI in chemistry.

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Area of Science:

  • Computational Chemistry and Cheminformatics
  • Explainable Artificial Intelligence (XAI)
  • Drug Discovery and Medicinal Chemistry

Background:

  • Feature attribution techniques in explainable artificial intelligence (XAI) help identify relevant input features for supervised learning models.
  • In molecular design, these methods often involve coloring molecular graphs to guide medicinal chemists in prioritizing compounds for synthesis.
  • Quantitative evaluation of molecular coloring approaches has been limited, primarily focusing on substructure identification.

Purpose of the Study:

  • To develop and present a novel benchmark for quantitatively evaluating molecular feature attribution techniques in drug design.
  • To compare the performance of classical machine learning models against graph neural network alternatives using this benchmark.
  • To facilitate the development and testing of new molecular feature attribution methods by providing open-source benchmark data.

Main Methods:

  • Developed a benchmark based on maximum common substructure algorithms applied to experimentally determined activity cliffs.
  • Applied molecule coloring approaches using both classical machine learning models and graph neural networks.
  • Quantitatively evaluated the performance of these attribution methods on the activity cliff benchmark.

Main Results:

  • The proposed benchmark demonstrates that molecule coloring approaches combined with classical machine learning models generally outperform more recent graph neural network alternatives.
  • Activity cliffs, crucial for understanding structure-activity relationships, serve as an effective basis for evaluating feature attribution methods.
  • The study provides a valuable, open-source dataset for the advancement of molecular feature attribution techniques.

Conclusions:

  • Classical machine learning models show strong performance in molecular feature attribution tasks when evaluated on activity cliffs, challenging the dominance of newer deep learning methods.
  • The developed benchmark provides a standardized and quantitative method for assessing the reliability of feature attribution techniques in drug design.
  • Open-sourcing the benchmark data will accelerate research and development in explainable AI for molecular discovery.