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Structure-Activity Relationships and Drug Design01:28

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Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative

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Activity cliffs in quantitative structure-activity relationship (QSAR) datasets limit predictivity more than experimental error or activity distribution. Metrics identifying these cliffs are crucial for assessing QSAR modelability.

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Cheminformatics

Background:

  • Quantitative structure-activity relationship (QSAR) model predictivity varies significantly across datasets.
  • Recent research suggests data limitations, specifically activity cliffs, rather than QSAR methodology, are the primary constraint on modelability.
  • Activity cliffs represent small structural changes leading to large activity changes, posing challenges for predictive modeling.

Purpose of the Study:

  • To investigate the predictive power of experimental error, activity distribution, and activity cliff metrics in determining QSAR dataset predictivity.
  • To evaluate the effectiveness of these metrics across diverse in-house datasets, including those with manipulated activity distributions and added noise.
  • To assess the performance of modern QSAR methods and alternative descriptors when dealing with activity cliffs.

Main Methods:

  • Analysis of in-house QSAR datasets with varying characteristics (unmodified, structurally determined, manipulated activity distributions, added noise).
  • Calculation and comparison of experimental error, activity distribution metrics, and activity cliff metrics.
  • Evaluation of QSAR model performance using random-split cross-validation and time-split validation.
  • Assessment of modern QSAR methods and alternative descriptors on activity cliff compounds.

Main Results:

  • Activity cliff metrics were superior predictors of dataset predictivity compared to experimental error and activity distribution metrics across all dataset types.
  • Activity cliff metrics struggled to differentiate real cliffs caused by high uncertainty in activity measurements.
  • Modern QSAR methods and alternative descriptors showed limited success in predicting activities for compounds on activity cliffs, aligning with modelability assumptions.
  • Time-split predictivity was related to random-split predictivity, with chemical space coverage being as critical as activity uncertainty and activity cliffs.

Conclusions:

  • Activity cliff metrics are essential for evaluating QSAR dataset modelability, outperforming other investigated metrics.
  • High uncertainty in activity measurements can obscure true activity cliffs, complicating their identification.
  • The limitations of current QSAR methodologies and descriptors in handling activity cliffs persist.
  • Both chemical space coverage and data quality (activity uncertainty and cliff presence) are critical factors limiting QSAR predictivity.