Tracing compound pathways using chemical space networks.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Cheminformatics

Background:

• Similarity-based compound networks (CSNs) represent chemical space using nodes for compounds and edges for similarity.
• CSNs facilitate visualization of structure-activity relationship (SAR) patterns by annotating nodes with activity data.
• The structure and topology of CSNs are significantly influenced by the chosen similarity measures.

Purpose of the Study:

• To introduce a novel type of CSN utilizing an asymmetric similarity metric based on maximum common substructure.
• To adapt CSNs for the identification of compound pathways within datasets, moving beyond SAR visualization.
• To enable systematic tracing of pathways composed of structurally related compounds, modeling optimization paths.

Main Methods:

• Development of a new CSN variant employing an asymmetric similarity metric.
• Utilizing maximum common substructure (MCS) as the basis for pairwise compound similarity.
• Systematic tracing and extraction of compound series forming optimization pathways within the network.

Main Results:

• The new CSN variant successfully identifies pathways of structurally related compounds with increasing size.
• These pathways serve as models for compound optimization paths in medicinal chemistry.
• The network-based approach facilitates the intuitive identification of hit-to-lead or lead optimization series.

Conclusions:

• The novel asymmetric CSN provides a powerful tool for identifying compound optimization pathways.
• This approach offers valuable insights for medicinal chemists in drug discovery and development.
• The method enhances the utility of CSNs for practical medicinal chemistry applications beyond SAR visualization.