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Updated: Oct 3, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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iANP-EC: Identifying Anticancer Natural Products Using Ensemble Learning Incorporated with Evolutionary Computation.

Loc Nguyen1, Thanh-Hoang Nguyen Vo2, Quang H Trinh1,3

  • 1Computational Biology Center, International University - VNU HCMC, Ho Chi Minh City 700000, Vietnam.

Journal of Chemical Information and Modeling
|February 14, 2022
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Summary

A new computational framework, iANP-EC, effectively identifies natural anticancer compounds. This machine learning approach aids in discovering novel cancer drugs with fewer side effects.

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Area of Science:

  • Computational chemistry and bioinformatics
  • Drug discovery and development
  • Natural product research

Background:

  • Cancer remains a leading cause of mortality globally, driving the search for novel, effective, and safer therapeutic agents.
  • Natural products represent a promising source for anticancer drug discovery, offering diverse chemical structures.
  • Traditional experimental methods for identifying anticancer compounds are complemented by computational approaches for virtual screening.

Purpose of the Study:

  • To develop a robust computational framework for identifying natural products with anticancer potential.
  • To integrate machine learning and evolutionary computation for enhanced predictive accuracy.
  • To provide a user-friendly tool for researchers to explore natural anticancer agents.

Main Methods:

  • Construction of an ensemble computational framework (iANP-EC) using four machine learning algorithms (k-NN, SVM, RF, XGB) and four molecular representations.
  • Selection of the top-four best-performing classifiers to form the ensemble.
  • Optimization of classifier weights using Particle Swarm Optimization (PSO).
  • Model training and validation using a curated dataset of 997 compounds from NPACT and CancerHSP databases.

Main Results:

  • The iANP-EC framework demonstrated high performance with an AUC-ROC of 0.9193 and AUC-PR of 0.8366.
  • The model proved to be stable, robust, and effective in predicting anticancer activities.
  • Analysis of molecular substructures identified key features associated with anticancer properties.
  • An online web server was developed to facilitate the identification of natural anticancer products.

Conclusions:

  • The iANP-EC framework offers a powerful and reliable computational tool for discovering natural anticancer agents.
  • The study highlights the potential of integrating machine learning with evolutionary computation in drug discovery.
  • Identifying key molecular substructures can guide the design of new anticancer drugs.
  • The developed web server supports the research community in accelerating the discovery of natural products with anticancer activities.