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Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database.

Mariusz Butkiewicz1, Edward W Lowe, Ralf Mueller

  • 1Department of Chemistry, Pharmacology, and Biomedical Informatics, Center for Structural Biology, Institute of Chemical Biology, Vanderbilt University, Nashville, TN 37232, USA.

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PubMed
Summary
This summary is machine-generated.

This study benchmarks ligand-based computer-aided drug discovery (LB-CADD) methods using public High-Throughput Screening (HTS) data. A new cheminformatics framework, BCL::ChemInfo, demonstrates effective Quantitative Structure-Activity Relationship (QSAR) model building for drug discovery.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • High-Throughput Screening (HTS) data is increasingly available in public domains like PubChem.
  • Ligand-based computer-aided drug discovery (LB-CADD) offers potential to accelerate academic drug discovery.
  • Benchmarking LB-CADD methods requires reliable, curated HTS datasets.

Purpose of the Study:

  • To establish a foundation for benchmarking LB-CADD methods.
  • To introduce and evaluate the BCL::ChemInfo cheminformatics framework.
  • To assess various machine learning algorithms and descriptor optimization techniques.

Main Methods:

  • Assembled nine curated HTS datasets from PubChem for major drug target families.
  • Built Quantitative Structure-Activity Relationship (QSAR) models using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen Networks (KNs).
  • Evaluated descriptor optimization protocols (e.g., Sequential Feature Forward Selection) and cross-validation techniques.

Main Results:

  • The BCL::ChemInfo framework was benchmarked using the curated datasets.
  • Various QSAR models were constructed and their predictive power assessed.
  • Observed enrichments ranging from 15 to 101 at a 25% True Positive Rate (TPR) cutoff.

Conclusions:

  • The developed datasets and BCL::ChemInfo framework provide a robust platform for LB-CADD method evaluation.
  • The study validates the utility of diverse machine learning algorithms for QSAR modeling in drug discovery.
  • The findings highlight the potential of LB-CADD approaches to enhance efficiency in identifying potential drug candidates.