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Related Experiment Video

Updated: Mar 17, 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

Graph-Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures.

Faisal Saeed^1,2, Naomie Salim³, Ammar Abdo^4,5

¹Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Malaysia. alsamet.faisal@gmail.com.

Molecular Informatics

|August 3, 2016

Summary

Consensus clustering enhances the separation of biologically active molecules from inactive ones by combining multiple chemical structure clusterings. Graph-based methods show improved effectiveness in this crucial task for drug discovery.

Keywords:

Compound selection Ensemble generations Graph partitioning High throughput Screening Individual clusterings Molecular dataset

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Last Updated: Mar 17, 2026

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Area of Science:

Bioinformatics
Machine Learning
Applied Statistics

Background:

Consensus clustering is a powerful technique for integrating multiple classifiers across various scientific domains.
Effective clustering of chemical structures is vital for distinguishing biologically active compounds from inactive ones.

Purpose of the Study:

To apply consensus clustering for combining chemical structure clusterings.
To enhance the separation of biologically active from inactive molecules within clusters.
To evaluate graph-based consensus clustering methods for this application.

Main Methods:

Two graph-based consensus clustering methods were investigated.
The Quality Partition Index (QPI) was used for evaluating clustering performance.
Results were compared against Ward's clustering method.
Experiments utilized homogeneous and heterogeneous subsets (DS1-DS2) from the MDDR database, represented by 2D fingerprints.

Main Results:

Consensus clustering, particularly graph-based approaches, improved the effectiveness of chemical structure clustering.
The combination of multiple individual clustering runs and a single run of multiple clusterings yielded positive results.
QPI effectively evaluated the performance of the consensus clustering methods.

Conclusions:

Graph-based consensus clustering methods offer a significant improvement in separating biologically active from inactive chemical structures.
This approach enhances the utility of clustering in drug discovery and related bioinformatics applications.
The findings support the broader application of consensus clustering in chemical informatics.