Matched Molecular Series: Measuring SAR Similarity.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Medicinal chemists intuitively assess structure-activity relationship (SAR) similarity for novel compound design.
• A quantitative and systematic method for comparing SAR similarity metrics is lacking.
• Accurate SAR similarity measurement is crucial for applications like matched molecular series (MMS) analysis.

Purpose of the Study:

• To develop and validate a statistical framework for comparing SAR similarity metrics.
• To identify the best metric for predicting novel substituent activity based on existing SAR data.
• To enhance the utility of MMS analysis in drug discovery.

Main Methods:

• Developed a two-step process: judging series similarity and transferring SAR.
• Tested various SAR similarity metrics within the statistical framework.
• Utilized centered RMSD (cRMSD) and linear regression-based prediction interpolation.

Main Results:

• The combination of cRMSD and linear regression-based prediction interpolation demonstrated superior ranking of SAR profiles.
• This approach enables confident suggestion of novel substituents for testing.
• The cRMSD metric's effectiveness is attributed to its handling of experimental uncertainty in affinity data.

Conclusions:

• A novel, quantitative method for measuring SAR similarity has been established.
• The proposed framework and cRMSD metric improve the prediction of novel compound activity.
• This work enhances MMS analysis as a tool for idea generation and semiquantitative prediction in medicinal chemistry.