Case studies

Activity landscape analysis for compound datasets

Drug discovery is one of the areas in which Structure–Property and Structure–Activity Relationship SPR/SAR has a large impact, wherein medicinal chemists analyze structure-pharmacokinetic relationships to optimize activity to pharmacological and toxicological systems. Read how Excelra's GOSTAR® was leveraged to analyze compounds datasets to generate SAR/SPR.

Overview

Activity landscape analysis (ALA) methods are essential emerging approaches used to systematically capture structure–property/activity relationships (SPRs/SARs). These studies have broad applications in multidisciplinary areas, especially in drug discovery, where medicinal chemists must analyze structure–pharmacokinetic relationships to optimize biological activity while improving the overall pharmaceutical profile of a lead series. Excelra’s global online structure activity relationship database, GOSTAR™, is a vital tool for this work, providing a 360-degree view of millions of compounds and linking their chemical structure to biological and pharmacological information. This allows users to quickly visualize and explore activity landscapes and evaluate complex structure–activity relationship (SAR) data (See our glossary for a definition of SAR). The in-built comprehensive “analyzer tool” within GOSTAR™ is designed specifically to generate high-quality SPR/SAR insights.

Our client

Our client

Our client base spans the globe, primarily targeting organizations that rely on high-quality SAR data and advanced analytical methods. These include:

  • Pharmaceuticals
  • Biotech research and academia
  • Artificial intelligence/machine learning-driven drug discovery companies
  • Contract research organizations (CROs)
Client’s challenge

Client’s challenge

The primary challenge for clients in medicinal chemistry and cheminformatics is efficiently managing and interpreting the massive volume of SAR data generated during hit identification and lead optimization. Traditional methods often fail to keep pace with the increasing structural diversity and potency distribution of hit compounds, leading to difficulties in discerning meaningful structure-activity relationships. This is a challenge often solved by leveraging rigorous in-house services, such as our dedicated Data curation offering. This lack of clear relationships can delay the multi-parameter optimization process crucial for drug development.

Client’s goals

Client’s goals

The main goal for clients leveraging GOSTAR’s analytical tools is to systematically and rapidly capture complex structure–activity relationships. This systematic method enables them to:

  • Quickly visualize and interpret activity distributions across large compound datasets.
  • Identify activity cliffs and smooth SAR regions to guide chemical modification strategies.
  • Streamline lead optimization by pinpointing compounds with improved potency and acceptable physiochemical properties.
  • Evaluate off-target activity early in the discovery phase to mitigate toxicity risks (learn more on the main GOSTAR page).

Our Approach

Excelra’s GOSTAR platform is designed by medicinal chemists for cheminformatics applications, offering a suite of five key analyzers that enable precise activity landscape analysis across compound datasets.

Activity analyzer

This tool provides a graphical representation of the activity distribution for an endpoint in relation to a single target from different sources. The output directly compares chemical substance vs. specific target vs. activity potency.

Off-target analyzer

The off-target analyzer is crucial for identifying a compound’s potency and selectivity against other targets. This systematic identification of potential Off-target activity helps to understand the polypharmacology of a compound, which is vital for effective risk mitigation during lead optimization.

Property space analyzer

This tool generates molecular encoding schemes from general chemical properties (e.g., lipophilicity, molecular weight, solubility, and total polar surface area). This analysis aids in the virtual screening of activity-enriched sets of molecules from synthetically accessible chemical space. The results can be effectively communicated through dedicated Visualization tools.

Target druggability analyzer

The target druggability analyzer helps identify the goodness of interaction between a chemical compound and its target protein. This provides a graphical representation of key molecular properties, such as ligand efficiency (LE) versus ligand lipophilic efficiency (LLE), providing quick insights for lead optimization. This is a core feature for our Medicinal chemistry teams who require quick, normalized metrics.

Molecular pair analyzer

Using this analyzer, we can describe the systematic method of identifying matched molecular pairs (MMPs) from a set of compounds and determining the property change associated (i.e., structural similarity vs. activity value differences). This is a powerful technique for understanding SAR data and identifying beneficial chemical transformations (Read more about our use of Molecular pair analysis).

Diagram illustrating the five key analyzers in GOSTAR (Activity, Off-Target, Property Space, Target Druggability, Molecular Pair) for SAR data exploration.
Activity landscape analysis concept showing the relationship between structural similarity and biological activity.

Conclusion

GOSTAR’s comprehensive analyzer tool kit provides medicinal chemistry teams and Cheminformatics experts with the necessary power and flexibility to perform sophisticated activity landscape analysis. By integrating high-quality, manually curated SAR data from GOSTAR with these specialized analytical tools, our clients are able to move beyond simple data retrieval. They can effectively explore their compound datasets, identify valuable activity cliffs, rationalize structural modifications, and accelerate the complex, multi-parameter optimization process inherent to successful drug discovery.