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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug-induced Sensitization of Adenylyl Cyclase: Assay Streamlining and Miniaturization for Small Molecule and siRNA Screening Applications
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Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform.

Matthew L Hudson1, Ram Samudrala1

  • 1Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA.

Molecules (Basel, Switzerland)
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Summary
This summary is machine-generated.

Shotgun drug repurposing platforms like CANDO can identify new uses for existing drugs. Hybrid pipelines combining different methods improve prediction accuracy for novel drug indications.

Keywords:
computational biologydrug repositioningdrug repurposingmolecular dockingmultiscalemultitargetingpolypharmacologyproteomic signaturestructural bioinformaticsvirtual screening

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Area of Science:

  • Computational drug discovery
  • Pharmacology
  • Bioinformatics

Background:

  • Drug repurposing accelerates therapeutic development by identifying new uses for existing medications.
  • Shotgun drug repurposing aims to systematically assess drugs across multiple clinical indications.
  • The Computational Analysis of Novel Drug Opportunities (CANDO) platform facilitates large-scale drug repurposing analysis.

Purpose of the Study:

  • To evaluate and benchmark hybrid decision tree pipelines for shotgun drug repurposing.
  • To compare the performance of hybrid pipelines against constituent docking-based pipelines.
  • To assess the CANDO platform's adaptability to different drug-signature generation methods.

Main Methods:

  • Calculated drug-proteome interaction signatures using Autodock Vina.
  • Developed hybrid decision tree pipelines integrating bio- and chem-informatic approaches.
  • Benchmarked pipeline performance based on known drug-indication associations.

Main Results:

  • The hybrid decision tree pipeline achieved higher indication accuracy (13.3% at top10 cutoff) than constituent pipelines (10.9% and 7.1%).
  • Performance surpassed random control accuracy (2.2%).
  • Demonstrated that the CANDO paradigm is independent of specific docking methods.

Conclusions:

  • Synthesizing multiple CANDO pipelines enhances drug repurposing predictive capabilities.
  • Varied docking-based signature generation methods capture unique signals for improved drug-proteome interaction signature comparison.
  • The CANDO platform shows robust performance and adaptability for efficient drug repurposing.