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LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening.

Jun Hu1,2, Zi Liu1, Dong-Jun Yu1

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.

Bioinformatics (Oxford, England)
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Summary
This summary is machine-generated.

We developed LS-align, a novel algorithm for atom-level ligand structural alignment, significantly improving virtual drug screening accuracy and efficiency by incorporating flexibility and chemical properties.

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Area of Science:

  • Computational chemistry
  • Structural bioinformatics
  • Drug discovery

Background:

  • Accurate structural alignment of small ligand molecules is crucial for computer-aided virtual drug screening.
  • Existing methods often rely on rigid-body shape comparison, lacking atom-specific information and flexibility, which are vital for high-throughput screening.

Purpose of the Study:

  • To introduce LS-align, a novel algorithm for fast and accurate atom-level structural alignment of ligand molecules.
  • To enable both rigid-body and flexible alignments for enhanced virtual screening capabilities.

Main Methods:

  • LS-align employs an iterative heuristic search to optimize a target function combining inter-atom distance, mass, and chemical bond comparisons.
  • It includes Rigid-LS-align and Flexi-LS-align modules for different alignment types.
  • A ligand-size independent, statistics-based scoring function evaluates molecular similarity.

Main Results:

  • LS-align demonstrated superior performance in large-scale benchmark tests on the DUD-E database, achieving an average enrichment factor (EF) of 22.0 at 1% cutoff and an AUC score of 0.75.
  • Performance significantly surpassed other state-of-the-art methods.
  • Analysis attributed the success to the target function's integration of structural and chemical information and the inclusion of structural flexibility.

Conclusions:

  • LS-align offers a significant advancement in ligand structural alignment, enhancing virtual screening efficiency.
  • The algorithm's ability to capture subtle molecular differences and conformational changes improves the identification of effective drug candidates.
  • This work presents a new approach to boost virtual screening effectiveness through sensitive ligand structural alignment.