3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine

Area of Science:

• * Cheminformatics and Bioinformatics
• * Computational Chemistry
• * Structural Biology

Background:

• * Analyzing protein-ligand interactions is crucial for drug discovery and understanding biological processes.
• * Existing tools often lack integration, hindering comprehensive analysis of complex structural and pharmacological data.
• * Proteome-wide databases and specialized information systems provide vast amounts of relevant data.

Purpose of the Study:

• * To present 3D-e-Chem-VM, an integrated open-source virtual machine for analyzing protein-ligand interaction data.
• * To provide a platform that combines cheminformatics and bioinformatics tools within a graphical programming environment.
• * To enable new approaches in virtual ligand screening, metabolism prediction, and ligand design.

Main Methods:

• * Development of an open-source Virtual Machine (3D-e-Chem-VM) integrating cheminformatics and bioinformatics software libraries.
• * Incorporation of database and workflow tools for analyzing and combining small molecule and protein structural information.
• * Creation of new data analytics tools and workflows for exploiting data from proteomewide databases (ChEMBLdb, PDB) and specialized systems (GPCRdb, KLIFS).

Main Results:

• * 3D-e-Chem-VM offers a unified research infrastructure for structural cheminformatics.
• * The platform supports novel applications including virtual ligand screening (Chemdb4VS), ligand-based metabolism prediction (SyGMa), and structure-based binding site analysis for ligand design (KRIPOdb).
• * Enables efficient exploitation of diverse structural and pharmacological protein-ligand interaction data.

Conclusions:

• * 3D-e-Chem-VM provides a powerful, integrated environment for advanced analysis of protein-ligand interactions.
• * The developed tools and workflows facilitate innovative drug discovery and design strategies.
• * This open-source platform enhances the accessibility and utility of complex biological and chemical data.