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Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning.

Tianshi Yu1,2, Tianyang Huang1, Leiye Yu3

  • 1Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China.

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|February 25, 2023
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Summary

This study developed quantitative structure-activity relationship (QSAR) models to identify new inhibitors for Cytochrome P450 17A1 (CYP17A1), an enzyme linked to cancer. The models show promise for developing novel anti-cancer drugs targeting CYP17A1.

Keywords:
CYP17A1Murcko scaffoldcheminformaticsprostate cancerquantitative structure–activity relationship

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Cytochrome P450 17A1 (CYP17A1) is crucial for steroidogenesis, producing dehydroepiandrosterone (DHEA).
  • Abnormal DHEA levels are implicated in prostatic and breast cancers, making CYP17A1 a significant drug target.
  • Developing effective CYP17A1 inhibitors is vital for anti-cancer therapeutic strategies.

Purpose of the Study:

  • To apply cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling for identifying novel CYP17A1 inhibitors.
  • To explore chemical space, Murcko scaffolds, and structure-activity relationships (SARs) for both steroidal and nonsteroidal inhibitors.
  • To establish predictive QSAR models to guide the discovery of potent anti-cancer agents targeting CYP17A1.

Main Methods:

  • Compiled a dataset of 962 CYP17A1 inhibitors from the ChEMBL database, comprising 279 steroidal and 683 nonsteroidal compounds.
  • Developed QSAR classification models using PubChem fingerprints and algorithms like Extra Trees and Random Forest.
  • Performed cheminformatic analyses, including scaffold analysis and identification of activity cliffs (ACs), for nonsteroidal inhibitors.

Main Results:

  • A QSAR classification model for steroidal inhibitors using Extra Trees achieved high accuracy (0.933 training, 0.818 cross-validation, 0.833 test).
  • Systematic analysis of nonsteroidal inhibitors revealed key chemical space features and representative scaffolds.
  • The best QSAR model for nonsteroidal inhibitors (Model VIII, Random Forest) demonstrated robust accuracy (0.96 training, 0.92 cross-validation, 0.913 test).

Conclusions:

  • The developed QSAR models provide a strong foundation for the rational design of novel CYP17A1 inhibitors.
  • Cheminformatic insights into steroidal and nonsteroidal inhibitors facilitate drug discovery efforts.
  • These findings are instrumental for advancing the development of targeted anti-cancer therapies against CYP17A1.