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'Brain aided' musk design.

Philip Kraft1

  • 1Givaudan Schweiz AG, Fragrance Research, Uberlandstrasse 138, CH-8600 Dübendorf.

Chemistry & Biodiversity
|December 29, 2006
PubMed
Summary

This review explores musk odorants, diverse in structure, challenging traditional computer models for predicting scent. New research investigates multiple musk receptors and designs novel linear musks.

Area of Science:

  • Organic Chemistry
  • Computational Chemistry
  • Olfactory Science

Background:

  • Musk odorants represent a classical area for structure-odor relationship (SOR) studies.
  • Unlike sandalwood or amber odorants, musks encompass diverse chemical classes (macrocycles, polycycles, nitroarenes).
  • Existing SOR models often focus on single classes, limiting predictive power due to structural diversity.

Framework:

  • Investigates the limitations of current SOR models that exclude structural diversity.
  • Examines how common synthetic pathways can create structural similarities unrelated to receptor binding.
  • Highlights the importance of specific structural features, using Galaxolide's (4S)-Me group as an example.

Implementation:

  • Synthesizes and olfactorily evaluates Galaxolide-like macrobicycles as conformationally constrained models.

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  • Explores the potential existence of multiple musk receptors through model compound analysis.
  • Discusses the 'brain-aided' design and conformational analysis of novel musk odorants, including linear musks.

    • Implications:

    • Demonstrates the need for SOR models that accommodate diverse chemical structures for improved predictability.
    • Provides insights into the conformational requirements for musk receptor interaction.
    • Introduces new families of musk compounds and analyzes key structural features influencing odor perception.