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Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to

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Machine learning models like AlphaFold2 show promise for drug discovery. However, using their predicted protein structures directly in virtual screening requires post-processing for accurate hit-finding.

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Area of Science:

  • Structural biology
  • Computational drug discovery
  • Protein structure prediction

Background:

  • Machine learning models, including AlphaFold2, have revolutionized protein structure prediction.
  • Their application in drug discovery, particularly virtual screening, is under active investigation.
  • Few studies have assessed virtual screening performance using models with limited template information.

Purpose of the Study:

  • To evaluate the utility of AlphaFold2-predicted protein structures, generated with minimal template data, for hit-finding in virtual screening.
  • To investigate the impact of post-processing on the accuracy of docking studies using these models.

Main Methods:

  • Developed a modified AlphaFold2 version excluding templates with >30% sequence identity.
  • Performed rigid receptor-ligand docking studies using the generated protein structures.
  • Compared results with and without post-processing modeling of the binding site.

Main Results:

  • Out-of-the-box AlphaFold2 models, even with low template identity, are suboptimal for direct virtual screening.
  • Post-processing modeling is crucial to refine the predicted binding site into a more realistic holo model for improved docking accuracy.
  • Previous work demonstrated quantitative accuracy with free energy perturbation methods.

Conclusions:

  • AlphaFold2 holds potential for drug discovery, but its direct application in virtual screening needs refinement.
  • Implementing post-processing steps to generate realistic holo models is essential for successful hit-finding campaigns.
  • Further research is warranted to optimize the use of AI-predicted structures in drug discovery pipelines.