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A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions.

Zhuo Yang1, Yingtao Liu, Zhaoqiang Chen

  • 1Drug Discovery and Design Center, Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China, zhuo.yang@sibcb.ac.cn.

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Summary

A new scoring function, XBScore(QM), accurately predicts halogen bonding strength using quantum mechanics. This tool enhances virtual screening and benchmarks other scoring functions for drug discovery.

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Drug discovery

Background:

  • Halogen bonding is a crucial non-covalent interaction in molecular recognition.
  • Accurate prediction of halogen bonding is essential for rational drug design.
  • Existing scoring functions often struggle to precisely characterize halogen bonding.

Purpose of the Study:

  • To develop a quantum mechanics-based scoring function for halogen bonding (XBScore(QM)).
  • To evaluate the performance of XBScore(QM) against established scoring functions.
  • To provide a tool for high-throughput virtual screening of halogen bonding interactions.

Main Methods:

  • Development of XBScore(QM) using 18,135 geometrical and energetical parameters.
  • Optimization at the M06-2X/aug-cc-pVDZ level of theory.
  • Application and validation on Protein Data Bank (PDB) datasets containing halogen bonding systems.

Main Results:

  • XBScore(QM) accurately predicts halogen bonding as an attractive force up to -4 kcal mol(-1).
  • Demonstrated superior performance over 12 other scoring functions in pseudo docking, ranking, scoring, and genuine docking power.
  • Systematic evaluation on diverse protein-ligand complexes confirmed its integrative advantage.

Conclusions:

  • XBScore(QM) is a practical tool for high-throughput virtual screening of halogen bonding.
  • The function serves as a benchmark for assessing other scoring functions' ability to characterize halogen bonding.
  • This work advances the computational prediction of halogen bonding in drug discovery and molecular recognition.