# Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships

## TL;DR

This study uses network models to analyze BACE-1 inhibitors, revealing how their structures and properties cluster, which could help in drug development.

## Contribution

The study introduces a novel framework using complex networks and multiple distance functions to cluster BACE-1 inhibitors based on molecular descriptors.

## Key findings

- Euclidean distance networks provide the most reliable clustering of BACE-1 inhibitors with minimal information loss.
- Eigenvector centrality analysis identifies key inhibitors acting as hubs in biochemical pathways.
- Community detection reveals distinct functional and structural groupings among BACE-1 inhibitors.

## Abstract

This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.