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PI3K-Seeker: A Machine Learning-Powered Web Tool to Discover PI3K Inhibitors.

Francisca Joseli Freitas de Sousa1, Dinler Amaral Antunes2, Geancarlo Zanatta1,3

  • 1Postgraduate Programme in Biochemistry, Department of Biochemistry at Federal University of Ceará, Fortaleza 60440-554, CE, Brazil.

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Summary

We developed PI3K-Seeker, a web server that efficiently identifies potential Phosphatidylinositol 3-kinases (PI3K) inhibitors. This tool aids in discovering new therapeutic agents for metabolic disorders and cancer.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Drug Discovery

Background:

  • Phosphatidylinositol 3-kinases (PI3Ks) are critical in human metabolism.
  • Dysregulation of PI3Ks is linked to metabolic disorders and cancer.
  • Discovering novel PI3K inhibitors is challenging and expensive.

Purpose of the Study:

  • To develop PI3K-Seeker, a web server for identifying novel PI3K inhibitors.
  • To create a two-stage prediction model for enhanced inhibitor screening.
  • To provide a user-friendly platform for large-scale compound analysis.

Main Methods:

  • Utilized a two-stage prediction process for inhibitor identification.
  • Employed the XGBoost algorithm for model training.
  • Extracted PubChem fingerprints from distinct datasets for feature engineering.

Main Results:

  • The first stage achieved high performance (MCC: 0.917, AUC-ROC: 0.993, ACC: 0.917).
  • The second stage demonstrated exceptional results (MCC: 0.939, AUC-ROC: 0.956, ACC: 0.994).
  • PI3K-Seeker effectively refines compound selection for potent inhibitors.

Conclusions:

  • PI3K-Seeker is a valuable tool for accelerating the discovery of PI3K inhibitors.
  • The web server offers a user-friendly interface for screening numerous compounds.
  • This approach can reduce the cost and time associated with drug discovery.