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Related Concept Videos

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Related Experiment Video

Updated: May 31, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble

Elisabetta Grazia Tomarchio1,2, Rocco Buccheri1, Antonio Rescifina1

  • 1Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy.

Journal of Chemical Information and Modeling
|May 29, 2026
PubMed
Summary

We developed a robust computational framework combining machine learning and docking to identify novel Indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors for cancer therapy. This approach enhances virtual screening accuracy and prioritizes drug candidates effectively.

Related Experiment Videos

Last Updated: May 31, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning in pharmacology

Background:

  • Indoleamine 2,3-dioxygenase 1 (IDO1) is a key enzyme in cancer immune evasion, making it a significant therapeutic target.
  • Despite its potential, clinical development of IDO1 inhibitors has faced challenges, necessitating improved drug discovery strategies.

Purpose of the Study:

  • To establish a reproducible, hierarchical virtual screening framework for prioritizing potent IDO1 inhibitors.
  • To integrate scaffold-aware machine learning with ensemble docking and molecular dynamics for enhanced prediction accuracy.

Main Methods:

  • A curated dataset of IDO1 inhibitors was processed and used to train machine learning models (Random Forest, XGBoost, SVM) with scaffold-based splitting.
  • Ensemble docking with GNINA and CNN rescoring was employed for prospective screening of FDA-approved drugs.
  • Molecular dynamics simulations were performed on top-ranked candidates to assess binding stability.

Main Results:

  • Machine learning models achieved high predictive performance (ROC-AUC ≈0.88-0.89), with applicability domain filtering ensuring reliability.
  • Prospective screening identified 39 potential IDO1 inhibitors among FDA-approved drugs.
  • Ensemble docking and molecular dynamics confirmed stable binding modes for top candidates, validating the screening framework's efficacy.

Conclusions:

  • The hierarchical integration of scaffold-aware machine learning and structure-based ensemble methods significantly improves the robustness of virtual screening.
  • This generalizable workflow effectively reduces false positives and supports reproducible candidate prioritization in computational drug discovery.
  • The study provides a fully executable Jupyter notebook for implementing the described virtual screening pipeline.