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Quantitative structure-property relationship modeling: a valuable support in high-throughput screening quality

Fiorella Ruggiu1, Patrick Gizzi, Jean-Luc Galzi

  • 1Laboratoire de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg , 1 rue Blaise Pascal, 67000 Strasbourg, France.

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Quantitative structure-property relationship (QSPR) modeling identified problematic molecules in a chemical library. Reanalysis revealed experimental issues, supporting QSPR for quality control in drug discovery screening.

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Cheminformatics

Background:

  • High-throughput screening (HTS) methods for evaluating pharmacokinetic properties like hydrophobicity are crucial but face challenges.
  • Accurate hydrophobicity data is essential for effective drug discovery and chemical library annotation.

Purpose of the Study:

  • To measure the chromatographic hydrophobicity index (CHI) for a subset of the French chemical library (Chimiothèque Nationale, CN).
  • To develop quantitative structure-property relationship (QSPR) models for annotating the CN.
  • To propose an algorithm for detecting and explaining outliers in QSPR predictions.

Main Methods:

  • Measurement of chromatographic hydrophobicity index (CHI) using HTS methods.
  • Development of QSPR models utilizing CHI data.
  • Implementation of an outlier detection algorithm for QSPR predictions.
  • Experimental re-evaluation of identified outlier molecules.

Main Results:

  • QSPR modeling successfully identified outlier molecules with significant prediction errors.
  • Experimental reanalysis of outliers revealed issues such as hydrolysis or structural absence, indicating data quality problems.
  • The developed QSPR models and outlier detection algorithm are available via a web server.

Conclusions:

  • QSPR modeling serves as a valuable support tool for quality control of HTS screening data.
  • Cooperation between experimental and theoretical teams is essential for improving the accuracy of screening data and QSPR models.
  • The study provides a validated approach for enhancing the reliability of chemical library data.