VAMMPIRE-LORD: a web server for straightforward lead optimization using matched molecular pairs.

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Drug discovery

Background:

• Improving lead compound binding affinity is crucial for drug development.
• Existing methods for rational drug design can be limited in scope and predictive power.

Purpose of the Study:

• To develop an innovative strategy, VAMMPIRE-LORD, for enhancing lead compound binding affinity.
• To leverage 3D matched molecular pairs (3D-MMPs) and a novel descriptor for predictive modeling.

Main Methods:

• Utilized 17,602 3D-MMPs to build a predictive model.
• Developed a novel atom-pair descriptor (LORD_FP) capturing ligand and receptor environments.
• Implemented VAMMPIRE-LORD as a user-friendly web server for guided lead optimization.

Main Results:

• The VAMMPIRE-LORD model successfully predicts the effect of chemical transformations on ligand affinity.
• Demonstrated the model's ability to extrapolate knowledge to similar chemical systems.
• The web server facilitates step-by-step lead compound optimization.

Conclusions:

• VAMMPIRE-LORD offers an effective computational approach for rational drug design.
• The method enhances lead optimization by predicting binding affinity improvements.
• The VAMMPIRE-LORD web server democratizes advanced lead optimization techniques.