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Exploring target-selectivity patterns of molecular scaffolds.

Ye Hu1, Jürgen Bajorath1

  • 1Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany.

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Summary

Researchers identified 42 highly target-selective molecular scaffolds from public data, revealing patterns for drug discovery. While selectivity data is sparse, these scaffolds offer promising starting points for further chemical exploration.

Keywords:
Molecular selectivitycompound database miningprivileged substructuresselectivity patternstarget family selective molecular scaffoldstarget-selective scaffolds

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Area of Science:

  • Medicinal Chemistry
  • Computational Drug Discovery
  • Pharmacology

Background:

  • Identifying target-selective molecular scaffolds is crucial for developing effective drugs with minimal side effects.
  • Existing compound activity data offers a potential resource for discovering such scaffolds.
  • The sparsity of selectivity data in public domains presents a challenge for scaffold identification.

Purpose of the Study:

  • To investigate the feasibility of identifying target-selective molecular scaffolds using public compound activity data.
  • To analyze selectivity patterns around specific human targets.
  • To provide starting points for future drug development efforts.

Main Methods:

  • Utilized a database of 17,745 public domain compounds with activity annotations for 433 human targets.
  • Employed a selectivity classification and database-mining approach.
  • Analyzed compound activity data to identify scaffolds selective for particular targets.

Main Results:

  • Identified 42 molecular scaffolds, each represented by multiple compounds, demonstrating high selectivity for specific targets.
  • Observed that individual compounds with unique scaffolds can also be target-selective.
  • Discovered selectivity patterns associated with specific targets, formed by multiple selective scaffolds.
  • Concluded that current public domain compound selectivity data is sparse.

Conclusions:

  • Publicly available compound activity data can yield target-selective molecular scaffolds.
  • The identified scaffolds and selectivity patterns serve as valuable starting points for medicinal chemistry exploration.
  • Further research is needed to expand the available selectivity data for more comprehensive drug discovery efforts.