Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Alchemical free energy calculations are crucial for predicting molecular properties.
• Planning these calculations often involves complex network constructions.
• Existing methods lack systematic tools for network manipulation and analysis.

Purpose of the Study:

• To introduce Konnektor, an open-source Python package for planning, modifying, and analyzing free energy calculation networks.
• To facilitate the drug discovery process by simplifying the setup of complex free energy campaigns.
• To provide a framework for integrating graph manipulation tools with network generation algorithms.

Main Methods:

• Utilizing graph theory to represent molecules as nodes and transformations as edges.
• Implementing network operations such as concatenation, deletion, and clustering.
• Developing visualization and analysis tools for network features.
• Comparing different network layout features using toy datasets.

Main Results:

• Konnektor offers systematic planning, modification, and analysis of free energy networks.
• The package includes functions for complex graph manipulations and network operations.
• Application examples demonstrate the utility and performance of Konnektor networks.
• The software is freely available under the MIT License.

Conclusions:

• Konnektor provides a robust and user-friendly platform for designing and analyzing free energy calculations.
• The package enhances efficiency and systematic approaches in computational chemistry for drug discovery.
• Its open-source nature promotes further development and integration within the scientific community.