Deciphering Zinc Binding Groups through Systematic Modeling and Cheminformatic Analysis

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Zinc metalloenzymes (Zn-MEs) are therapeutically significant targets.
• Current drug discovery for Zn-MEs is limited by the structural diversity of zinc-binding groups (ZBGs).

Purpose of the Study:

• To identify novel zinc-binding groups (ZBGs) for targeting zinc metalloenzymes (Zn-MEs).
• To develop a ZBG library to facilitate Zn-ME inhibitor discovery and optimization.

Main Methods:

• Systematic modeling and cheminformatic analysis of known Zn-ME inhibitors.
• Application of twelve binary classifiers to distinguish Zn-ME inhibitors.
• Utilizing interpretable machine learning and substructure detection to define ZBGs.
• Creation of a ZBG library with 41 SMARTS patterns.

Main Results:

• A novel ZBG library containing 41 SMARTS patterns was generated.
• The library demonstrated effectiveness in enriching potential Zn-ME inhibitors.
• The library aided in optimizing drug-likeness and modulating subtype selectivity.

Conclusions:

• The developed ZBG library expands the toolkit for discovering novel Zn-ME inhibitors.
• This approach addresses the challenge of limited ZBG diversity in drug discovery.
• The findings are expected to accelerate the development of new therapeutics targeting Zn-MEs.