SAR (Structure- Activity Relationship) Analysis

SAR (Structure- Activity Relationship) is a critical part of drug discovery and is useful in multiple stages during the process of drug discovery. SAR analysis is used for primary screening and well as lead optimization later on in the drug discovery pipeline. SAR Analysis involves various features that aid in understanding the relationship between chemical structure and activity. Molsoft offers in-depth options for analyzing SAR

It includes generating SAR tables for a set of ligand molecules to identify this relationship. Additionally, R-decomposition of the dataset, based on a specified scaffold can be performed, which generates a table comprising multiple R-group attachments on the specified scaffold. This helps in examining the effect of functional groups on activity values.

SAR Analysis also facilitates the identification of “structure-activity cliffs” and enables Matched Pair Analysis (MPA) to study changes in chemical properties through small structural modifications. The method involves clustering and identifying the Maximum Common Substructure for each pair in each cluster, providing insights into R-group pair variation.

R-Group Decomposition of set of compounds

To decompose a library of compounds into fragments (multiple R-groups) based on a Markush scaffold, ICM offers the flexibility to create separate tables for each R-group or merge them into a single table, where each column represents a different R-group like R1 and R2. This is particularly handy if you wish to generate a SAR table with activity data alongside the R1 and R2 columns. Additionally, ICM provides the option to generate all possible R-groups present in the provided scaffold, enhancing the comprehensiveness of the analysis.

The Free Wilson Regression Analysis :

It is a quantative method for describing SAR, originally published in 1964. It is a unique method that can uncover intriguing combinations of substituents, potentially overlooked by other SAR analysis technique.

This approach involves assigning weights to the original R_group_weights table and color-coding them: blue indicates a positive contribution to the predicted property, while red indicates a negative contribution. The method returns separate tables, making it simpler to sort and identify which group holds the most influence in a specific position.

SAR Analysis

Create a SAR table where you can select one or more R-groups for analysis. The resulting table will be color-coded based on activity levels. The SAR table will display an activity scale and R-group scaffold in the first cell (A1), with the R-groups represented along each axis. This table allows for investigation into which R-group(s) contribute to activity.

SALI (Structure−Activity Landscape Index) analysis 

SALI analysis is another method for analyzing the structure-activity relationship. SALI technique was published by Rajarshi Guha and Jonn Van Drie in 2008. and they provided a simple way of identifying pairs of compounds where a small change in chemical structure brings about a large change in biological activity or physical properties. This in turn is very useful in identifying the parts of the molecule that are critical for its activity. Once we identify the groups then we can also analyze the interactions they are making with the receptor in case we are doing structure-based design. Also knowing these vital parts of the molecule and interactions, we can ensure that we preserve these while synthesizing them.

Users can perform SALI analysis in Molsoft’s ICM VLS or ICM Chemist Pro. The Pair of molecules are shown in the plot encircled by a bigger circle denoting the SALI score. Red color represents the highest change in activity value.

Matched Pair Analysis (MPA)

It is a method employed to examine alterations in chemical properties resulting from precise, small-scale structural modifications to a compound’s structure. This process entails identifying the Maximum Common Substructure and grouping molecules based on similarity. The analysis involves two main stages: clustering and subsequently identifying the Maximum Common Substructure for each pair within each cluster. The output closely resembles SALI output but additionally provides insights into the specific variations between R-group pairs.

Once the user runs the MPA in Molsoft’s ICM VLS or ICM Chemist Pro, they will get a result table. The table is fully interactive between the plot and the original table. By default, the hits are sorted by the score column, so the pairs that show biggest activity change with smaller groups are placed on top of the table. The plot shown in the picture displays activity 1 versus activity 2 and is colored and sized by score.

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