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Updated: May 12, 2025

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Advancing promiscuous aggregating inhibitor analysis with intelligent machine learning classification.

Luxuan Wang1, Beihong Ji1, Jingchen Zhai1

  • 1Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 3501 Terrace St., Pittsburgh, PA 15261, United States.

Briefings in Bioinformatics
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately identify aggregating compounds, reducing false positives in drug discovery. A new interpretation method, global sensitivity analysis (GSA), efficiently pinpoints key molecular descriptors for improved screening library design.

Keywords:
colloidal aggregatorcompound library designdrug screeningglobal sensitivity analysismachine learning

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Small molecules are vital in drug discovery but can form colloidal aggregators, leading to false positives during hit screening.
  • These false positives increase research costs and time investment, necessitating early-stage identification methods.

Purpose of the Study:

  • To develop accurate machine learning classification models for identifying promiscuous aggregating inhibitors early in the drug discovery process.
  • To propose and validate a new model interpretation method, global sensitivity analysis (GSA), for identifying critical predictive descriptors.

Main Methods:

  • Trained classification models using a dataset of 10,000 aggregators and 10,000 nonaggregators.
  • Combined four molecular representations with various machine learning algorithms, including cubic support vector machines and path-based FP2 fingerprints.
  • Employed SHapley Additive exPlanations and proposed global sensitivity analysis (GSA) for model interpretation.

Main Results:

  • The best model combined path-based FP2 fingerprints with a cubic support vector machine, achieving high accuracy and area under the receiver operating characteristic curve (>0.93) on validation and test datasets.
  • Global sensitivity analysis (GSA) proved to be a time-efficient and accurate method for identifying crucial descriptors, especially with large datasets and limited descriptors.
  • High sensitivity and specificity levels (>0.93) were maintained by the best-performing model.

Conclusions:

  • Machine learning models, particularly those using FP2 fingerprints and cubic SVM, can effectively identify potential aggregating compounds, minimizing false positives in drug discovery.
  • GSA offers a valuable, efficient approach for model interpretation, complementing existing methods like SHapley Additive exPlanations.
  • These findings provide guidance for designing screening libraries to reduce false positives and optimize drug discovery pipelines.