Structural insights and molecular profiling of a large set of diverse compounds targeting PPARγ: from comprehensive cheminformatics approach to tool development.

Abstract

This study integrates a robust cheminformatics approach (including chemical space exploration, Bayesian model-based fingerprint analysis, and cluster-driven molecular profiling) to reveal the key structural features influencing peroxisome proliferator activated receptor-gamma (PPARγ) modulatory activity. The Bayesian classification model effectively differentiates between the beneficial and adverse structural characteristics of PPARγ modulators. Structural motifs such as substituted benzylamine, phenoxyphenyl groups, 5-phenyl-1,3-thiazolidine scaffolds, and indole rings have been identified as positive contributors, enhancing PPARγ activity. Conversely, features like substituted tertiary amines and sulphonamide groups were found to have detrimental effects, suggesting that these should be deprioritized in the design of future PPARγ modulators. Additionally, molecular clustering provided a means to categorize structurally similar compounds, facilitating scaffold analysis, diversity calculation, and lead optimization for PPARγ modulators. To extend these findings to the broader scientific community, we have developed an open-access online tool, 'Fasda_v1.0', (https://fasdav1web.streamlit.app/) designed for cluster-driven molecular profiling of any dataset, enabling further exploration and application of these methods. This study offers valuable guidance for designing and developing novel therapeutics targeting PPARγ, thereby contributing to advancements in treating type 2 diabetes mellitus and related diseases.