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Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.

Sema Atasever1

  • 1Department of Computer Engineering, Faculty of Engineering and Architecture, Nevsehir Haci Bektas Veli University, 50300 Nevşehir, Turkey.

International Journal of Molecular Sciences
|March 27, 2025
PubMed
Summary

Machine learning models accurately classify Hepatitis C virus (HCV) NS3 inhibitors using optimized molecular fingerprints. This computational approach enhances virtual screening for developing new antiviral drugs.

Keywords:
HCV NS3 inhibitorsQSARcomputational drug designmachine learningmolecular descriptor optimization

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Classifying Hepatitis C virus (HCV) NS3 inhibitors is crucial for identifying antiviral agents.
  • Computational methods, particularly machine learning, offer efficient approaches for drug discovery.

Purpose of the Study:

  • To develop an optimized machine learning model for classifying HCV NS3 inhibitors.
  • To evaluate the performance of random forest (RF) with molecular fingerprints for virtual screening.

Main Methods:

  • A dataset of 290 bioactive compounds was used for model training, retrieved from the ChEMBL database.
  • Twelve molecular fingerprint descriptors were tested, with the CDK graph-only fingerprint showing optimal performance.
  • Random forest (RF) classifier was optimized and compared with other models like IBk, LR, AdaBoost, and OneR using WEKA.

Main Results:

  • The optimized RF model achieved high accuracy (89.6552%) on the test set.
  • Key performance metrics included MAE of 0.2114, RMSE of 0.3304, and MCC of 0.7950.
  • The CDK graph-only fingerprint demonstrated superior performance in classifying HCV NS3 inhibitors.

Conclusions:

  • Optimized molecular fingerprints significantly enhance virtual screening for HCV inhibitors.
  • The developed machine learning approach provides a data-driven strategy for accelerating antiviral drug discovery.
  • This study validates the effectiveness of machine learning in identifying potential therapeutic agents against HCV.