Chemo-structural diversity of anti-obesity compound database

Area of Science:

• Natural product chemistry
• Chemoinformatics
• Drug discovery

Background:

• Nature is a significant source for novel therapeutics.
• The Anti-obesity Compound Database (AOCD) compiles natural small molecules with anti-obesity potential.
• The database includes compounds targeting pancreatic lipase (PL), appetite suppression (AS), and adipogenesis (AD).

Purpose of the Study:

• To analyze the diversity of natural anti-obesity compounds within the AOCD.
• To identify datasets with the highest structural and chemical diversity for drug discovery.
• To facilitate the identification of novel anti-obesity drug leads.

Main Methods:

• Utilized the Anti-obesity Compound Database (AOCD) containing 349 compounds from diverse natural sources.
• Performed chemoinformatic analysis on three distinct datasets (PL, AS, AD) using the Platform for Unified Molecular Analysis (PUMA).
• Evaluated compound diversity based on molecular descriptors, scaffold diversity, and structural fingerprint similarity.

Main Results:

• The pancreatic lipase (PL) dataset exhibited the highest compound diversity.
• Diversity was assessed using Euclidean distance for molecular properties and pairwise similarity for fingerprints.
• The analysis highlighted significant structural and scaffold variety within the PL dataset.

Conclusions:

• The PL dataset is a rich source for discovering novel anti-obesity agents.
• Chemoinformatic analysis of the AOCD provides valuable insights into natural compound diversity.
• This study accelerates the identification and development of new anti-obesity drugs from natural sources.