A Binary Ant Colony Optimization Classifier for Molecular Activities

Hammann, F. and Suenderhauf, C. and Huwyler, J.. (2011) A Binary Ant Colony Optimization Classifier for Molecular Activities. Journal of Chemical Information and Modeling, Vol. 51. pp. 2690-2696.

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Official URL: http://edoc.unibas.ch/dok/A5849088

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Chemical fingerprints encode the presence or absence of molecular features and are available in many large databases. Using a variation of the Ant Colony Optimization (ACO) paradigm, we describe a binary classifier based on feature selection from fingerprints. We discuss the algorithm and possible cross-validation procedures. As a real-world example, we use our algorithm to analyze a Plasmodium falciparum inhibition assay and contrast its performance with other machine learning paradigms in use today (decision tree induction, random forests, support vector machines, artificial neural networks). Our algorithm matches established paradigms in predictive power, yet supplies the medicinal chemist and basic researcher with easily interpretable results. Furthermore, models generated with our paradigm are easy to implement and can complement virtual screenings by additionally exploiting the precalculated fingerprint information.
Faculties and Departments:05 Faculty of Science > Departement Pharmazeutische Wissenschaften > Pharmazie > Pharmazeutische Technologie (Huwyler)
UniBasel Contributors:Huwyler, Jörg and Hammann, Felix and Suenderhauf, Claudia
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Chemical Society
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:14 Sep 2012 07:17
Deposited On:14 Sep 2012 06:40

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