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Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S.

Yuwei Yang1, Jianing Lu1, Chao Yang1

  • 1Department of Chemistry, New York University, New York, NY, 10003, USA.

Journal of Computer-Aided Molecular Design
|November 16, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning enhances Cathepsin S inhibitor ranking by improving docking strategies. This approach addresses challenges posed by large, flexible molecules, significantly boosting prediction accuracy for drug discovery.

Keywords:
DockingFragmentationMachine learningScoring functionVirtual screening

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Area of Science:

  • Biochemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Cathepsin S (CatS) is implicated in diseases like arthritis, cancer, and cardiovascular conditions.
  • CatS inhibitors present challenges in drug discovery due to their large size, flexibility, and structural similarity.
  • CatS inhibitors were evaluated in D3R-GC3 and D3R-GC4 for pose prediction and binding affinity ranking.

Purpose of the Study:

  • To develop an improved machine learning-based protocol for ranking Cathepsin S inhibitors.
  • To address the difficulties in docking and scoring large, flexible CatS inhibitors.
  • To enhance the accuracy of binding affinity predictions for CatS inhibitors.

Main Methods:

  • Utilized a similarity-based alignment docking and Vina scoring protocol for initial submissions.
  • Developed a machine learning approach with a curated CatS-specific training set.
  • Implemented a similarity-based constrained docking method and an arm-based fragmentation strategy.
  • Employed structure-based ranking protocols for inhibitor evaluation.

Main Results:

  • Initial Vina-based protocol achieved Kendall's τ of 0.23 for 459 binders in D3R-GC4.
  • Machine learning protocol with curated data and advanced docking/fragmentation achieved Kendall's τ of 0.52.
  • Demonstrated significant improvement in inhibitor ranking accuracy.

Conclusions:

  • Machine learning, combined with tailored training data and advanced docking strategies, significantly improves Cathepsin S inhibitor ranking.
  • The study highlights the critical role of training data curation, docking approaches, and fragmentation strategies in developing effective inhibitor-ranking protocols.
  • The developed methods offer a promising avenue for more accurate drug discovery targeting Cathepsin S.