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PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) protocol: a brief evaluation.

Sorin Avram1, Liliana M Pacureanu, Edward Seclaman

  • 1Department of Computational Chemistry, Institute of Chemistry of Romanian Academy, Timisoara, Mihai Viteazul Avenue, 24, 300223 Timisoara, Romania.

Journal of Chemical Information and Modeling
|November 10, 2011
PubMed
Summary

This study introduces PLSDA-DOCET, a novel consensus scoring technique for drug design. This approach enhances ligand binding energy estimation, outperforming traditional docking and similarity searches for drug discovery.

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Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Drug discovery

Background:

  • Docking studies are crucial in drug design for estimating ligand-protein binding energy using scoring functions.
  • Enhancing scoring function performance often involves combining multiple functions into a consensus scoring function.

Purpose of the Study:

  • To evaluate a target-oriented consensus technique (PLSDA-DOCET) using energetic terms from multiple scoring functions.
  • To compare PLSDA-DOCET's optimization strategies against classical rigid docking and ligand-based similarity searches (2D fingerprints, ROCS).

Main Methods:

  • Implemented PLSDA-DOCET, a consensus scoring approach utilizing energetic terms from various scoring functions.
  • Optimized consensus energetic terms and scoring functions using the Receiver Operating Characteristic (ROC) metric.
  • Compared PLSDA-DOCET performance with rigid docking and ligand-based similarity search methods (2D fingerprints, ROCS).

Main Results:

  • PLSDA-DOCET, optimized via an Area Under the Curve (AUC)-based strategy, demonstrated superior performance in retrieval and scaffold-hopping compared to other docking methods.
  • The enhanced performance of PLSDA-DOCET was validated on an external dataset, outperforming single and combined scoring functions.
  • A low mean correlation was observed in the ranks of chemotypes retrieved by PLSDA-DOCET compared to other methods.

Conclusions:

  • PLSDA-DOCET represents a significant advancement in consensus scoring for drug discovery, offering improved accuracy and reliability.
  • The AUC-based optimization strategy is effective for enhancing the performance of consensus scoring functions in virtual screening.
  • PLSDA-DOCET shows promise for identifying novel drug candidates and facilitating scaffold-hopping in drug design pipelines.