A Concise Review of Biomolecule Visualization

Li, Hui; Wei, Xinru

doi:10.3390/cimb46020084

CIMB

2024

DOI: 10.3390/cimb46020084

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A Concise Review of Biomolecule Visualization

Hui Li,

Xinru Wei

Abstract: The structural characteristics of biomolecules are a major focus in the field of structural biology. Molecular visualization plays a crucial role in displaying structural information in an intuitive manner, aiding in the understanding of molecular properties. This paper provides a comprehensive overview of core concepts, key techniques, and tools in molecular visualization. Additionally, it presents the latest research findings to uncover emerging trends and highlights the challenges and potential directions f… Show more

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2024

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Cited by 1 publication

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Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning

Akgüller,

Balcı,

Cioca

2024

Molecules

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This study conducts an in-depth analysis of clustering small molecules using spectral geometry and deep learning techniques. We applied a spectral geometric approach to convert molecular structures into triangulated meshes and used the Laplace–Beltrami operator to derive significant geometric features. By examining the eigenvectors of these operators, we captured the intrinsic geometric properties of the molecules, aiding their classification and clustering. The research utilized four deep learning methods: Deep Belief Network, Convolutional Autoencoder, Variational Autoencoder, and Adversarial Autoencoder, each paired with k-means clustering at different cluster sizes. Clustering quality was evaluated using the Calinski–Harabasz and Davies–Bouldin indices, Silhouette Score, and standard deviation. Nonparametric tests were used to assess the impact of topological descriptors on clustering outcomes. Our results show that the DBN + k-means combination is the most effective, particularly at lower cluster counts, demonstrating significant sensitivity to structural variations. This study highlights the potential of integrating spectral geometry with deep learning for precise and efficient molecular clustering.

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Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning

Akgüller,

Balcı,

Cioca

2024

Molecules

View full text Add to dashboard Cite

show abstract

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A Concise Review of Biomolecule Visualization

Cited by 1 publication

References 81 publications

Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning

Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning

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