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Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the

Luxuan Wang1, Xibing He1, Beihong Ji1

  • 1Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.

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Summary

This study introduces a new computational method, ANI-2x/CG-BS, that significantly improves molecular docking accuracy. The enhanced protocol boosts the success rate of identifying drug candidates and refines binding pose predictions for drug discovery.

Keywords:
ANI-2x potentialdocking protocolvirtual screening

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Molecular Modeling

Background:

  • Structure-based virtual screening is vital for drug discovery, but accurately predicting binding affinity and poses remains challenging.
  • Existing molecular docking programs face limitations in precision for ligand-macromolecule interactions.
  • Accurate prediction of binding modes and affinities is crucial for identifying viable drug candidates.

Purpose of the Study:

  • To introduce a novel computational protocol combining geometry optimization with machine learning potential for enhanced molecular docking.
  • To improve the accuracy of binding pose prediction and scoring in structure-based virtual screening.
  • To evaluate the performance of the new protocol compared to existing docking methods.

Main Methods:

  • Developed a novel protocol integrating the conjugate gradient with backtracking line search (CG-BS) geometry optimization algorithm with the ANI-2x machine learning potential.
  • Applied the ANI-2x/CG-BS protocol for structural optimization and potential energy prediction on small molecule-macromolecule and peptide-macromolecule systems.
  • Integrated the protocol with Glide for binding pose prediction and assessed its performance in optimizing and ranking ligands.

Main Results:

  • The ANI-2x/CG-BS protocol demonstrated improved optimization of binding poses, especially when initial predictions had high RMSD.
  • Achieved a 26% higher success rate in identifying native-like binding poses at the top rank compared to Glide docking alone.
  • Significantly enhanced scoring and ranking power, with Pearson's and Spearman's correlation coefficients increasing from 0.24/0.14 to 0.85/0.69 for ligand ranking.

Conclusions:

  • The novel ANI-2x/CG-BS protocol offers superior performance in molecular docking compared to traditional methods.
  • This enhanced protocol shows significant potential for integration into virtual screening pipelines to accelerate drug discovery.
  • The method provides more accurate binding pose predictions and improved ligand ranking, aiding in the identification of potential drug candidates.