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Updated: Mar 3, 2026

Split Hybridization Probe Utilizing a DNA Fluorescent Light-up Aptamer as a Signal Reporter for Sequence-Specific Nucleic Acid Analysis

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Matched Molecular Series: Measuring SAR Similarity.

Emanuel S R Ehmki¹, Christian Kramer¹

¹Chemical Biology/Therapeutic Modalities, F. Hoffmann-La Roche Ltd. , Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland.

Journal of Chemical Information and Modeling

|May 2, 2017

Summary

This summary is machine-generated.

Medicinal chemists can now quantitatively measure structure-activity relationship (SAR) similarity to predict novel compound activity. A new statistical framework using centered RMSD (cRMSD) and linear regression improves compound design and matched molecular series analysis.

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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.