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Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets.

Ganesh Chandan Kanakala1, Rishal Aggarwal1, Divya Nayar2

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Machine learning models for drug design are hindered by data biases. Addressing sequence, protein-ligand, and pocket similarity in data splits is crucial for reliable binding affinity prediction and virtual screening.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Protein-ligand interaction scoring functions are vital for drug design.
  • Machine learning models are increasingly used for developing these functions.
  • Existing datasets may contain hidden biases affecting model utility.

Purpose of the Study:

  • To systematically investigate biases in protein-ligand binding affinity datasets.
  • To highlight factors affecting the performance of machine learning models.
  • To provide guidance for developing reliable binding affinity predictors.

Main Methods:

  • Systematic investigation of published methods.
  • Analysis of sequence, protein-ligand interaction, and pocket structure similarity.
  • Examination of data splitting strategies.

Main Results:

  • Identified hidden biases in publicly available datasets.
  • Demonstrated the impact of data biases on model performance.
  • Explained high performance in protein-only and ligand-only models due to dataset characteristics.

Conclusions:

  • Considering sequence, interaction, and pocket similarity is essential for robust data splitting.
  • Awareness of dataset biases is critical for practical applications like virtual screening.
  • Recommendations provided for designing better binding affinity predictors and datasets.