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Updated: May 16, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Voting-based consensus clustering for combining multiple clusterings of chemical structures.

Faisal Saeed¹, Naomie Salim, Ammar Abdo

¹Faculty of Computer Science and Information Systems, University Technology of Malaysia, Johor, Malaysia. alsamet.faisal@gmail.com.

Journal of Cheminformatics

|December 19, 2012

Summary

Consensus clustering methods improve chemical structure analysis by combining multiple clustering results. The cumulative voting-based aggregation algorithm (CVAA) showed the best performance in separating biologically active molecules.

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Spatial Separation of Molecular Conformers and Clusters

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Last Updated: May 16, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Area of Science:

Computational chemistry
Bioinformatics
Machine learning

Background:

Consensus clustering is widely used in machine learning and bioinformatics.
Few methods have been applied to combine multiple clusterings of chemical structures.
Individual clustering methods may not yield optimal results for all datasets.

Purpose of the Study:

To apply voting and graph-based consensus clustering to chemical structure data.
To enhance the separation of biologically active from inactive molecules within clusters.
To evaluate the effectiveness of consensus clustering for chemical structure analysis.

Main Methods:

Examined cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA), and hyper-graph partitioning algorithm (HGPA).
Evaluated clusterings using F-measure and Quality Partition Index (QPI).
Compared consensus methods against Ward's clustering using the MDL Drug Data Report (MDDR) dataset with ALOGP and ECFP_4 fingerprints.

Main Results:

Voting-based consensus clustering outperformed Ward's method for both fingerprints using F-measure and QPI.
Graph-based consensus clustering outperformed Ward's method for ALOGP using QPI.
Jaccard and Euclidean distances were optimal for ensemble generation.

Conclusions:

Consensus clustering methods effectively improve chemical structure clustering.
The cumulative voting-based aggregation algorithm (CVAA) was the most effective consensus method tested.