inSARa: intuitive and interactive SAR interpretation by reduced graphs and hierarchical MCS-based network navigation

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Structure-Activity Relationship (SAR) analysis is crucial for drug discovery.
• Existing SAR methods often lack intuitive interpretation.
• Novel, user-friendly approaches are needed for effective SAR analysis.

Purpose of the Study:

• To introduce the intuitive networks for Structure-Activity Relationships analysis (inSARa) method.
• To provide an intuitive approach for SAR interpretation using network structures.
• To demonstrate the utility of inSARa in various drug discovery tasks.

Main Methods:

• Utilizes reduced graphs (RG) and the maximum common substructure (MCS) concept.
• Employs a hierarchical network structure based on common pharmacophoric features.
• Enables interactive network navigation for SAR interpretation.

Main Results:

• inSARa networks facilitate straightforward SAR interpretation.
• The method effectively identifies activity cliffs and "activity switches".
• It aids in analyzing bioisosteric exchanges and common pharmacophoric features.

Conclusions:

• The inSARa method offers a valuable, intuitive tool for SAR analysis.
• It enhances the understanding of molecular interactions and activity.
• inSARa supports critical tasks like identifying "SAR hotspots" in drug discovery.