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Related Experiment Videos

Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking.

Chao Yang1, Yingkai Zhang1,2

  • 1Department of Chemistry, New York University, New York, New York 10003, United States.

Journal of Chemical Information and Modeling
|September 1, 2021
PubMed
Summary

We developed Lin_F9, a novel scoring function for molecular docking, enhancing drug design accuracy. It outperforms existing methods in scoring and ranking protein-ligand interactions, particularly metal-ligand bonds.

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Area of Science:

  • Computational Chemistry
  • Structural Biology
  • Drug Discovery

Background:

  • Molecular docking is crucial for structure-based drug design.
  • Scoring functions are vital for docking accuracy and robustness.
  • Accurate scoring of metal-ligand interactions remains challenging.

Purpose of the Study:

  • Introduce Lin_F9, a new empirical scoring function.
  • Improve the accuracy and robustness of molecular docking.
  • Specifically enhance the description of metal-ligand interactions.

Main Methods:

  • Developed Lin_F9 as a linear combination of nine empirical terms.
  • Included a unified metal bond term for metal-ligand interactions.
  • Optimized parameters using a multistage fitting protocol with explicit water.

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Main Results:

  • Lin_F9 achieved top scoring power on the CASF-2016 benchmark for crystal and optimized poses (R=0.680, 0.687).
  • Consistently outperformed Vina in scoring and ranking power across diverse protein-ligand complexes.
  • Demonstrated superior performance in flexible docking scenarios and D3R GC4 test sets.

Conclusions:

  • Lin_F9 offers enhanced accuracy and robustness for molecular docking.
  • The function shows significant improvements in predicting protein-ligand binding.
  • Lin_F9 is implemented in Smina and available for broader use in drug design.