Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design.

Area of Science:

• Computational chemistry and cheminformatics
• Artificial intelligence in drug discovery
• Topological data analysis

Background:

• Artificial intelligence (AI) offers transformative potential for pharmaceutical industries, particularly in drug design.
• A critical challenge in AI-driven drug design is the development of efficient and transferable molecular descriptors or fingerprints.
• Existing mathematical representations struggle to capture the complexity of molecular structures and atomic interactions.

Purpose of the Study:

• To present a novel hypergraph-based topological representation for molecular structures and interactions.
• To develop hypergraph-based (weighted) persistent cohomology (HPC/HWPC) and associated molecular fingerprints for machine learning models.
• To enhance AI-based drug design by improving protein-ligand binding affinity prediction.

Main Methods:

• Development of the first hypergraph-based topological framework to characterize detailed molecular structures and atomic interactions.
• Construction of hypergraph-based persistent cohomology (HPC/HWPC) inspired by path complex models.
• Generation of molecular descriptors using HPC/HWPC for machine learning models in protein-ligand binding affinity prediction.

Main Results:

• The HPC/HWPC models were tested on three standard databases: PDBbind-v2007, PDBbind-v2013, and PDBbind-v2016.
• The proposed models significantly outperformed all existing machine learning models that utilize traditional molecular descriptors.
• Demonstrated superior performance in predicting protein-ligand binding affinity, a crucial step in drug design.

Conclusions:

• The hypergraph-based topological representation (HPC/HWPC) provides a powerful new approach for characterizing molecular complexity.
• HPC/HWPC-based molecular fingerprints offer enhanced efficiency and transferability for machine learning in drug design.
• This framework shows substantial promise for advancing AI-based drug discovery and development.