A binary ant colony optimization classifier for molecular activities.
J Chem Inf Model
; 51(10): 2690-6, 2011 Oct 24.
Article
em En
| MEDLINE
| ID: mdl-21854036
ABSTRACT
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.
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1
Base de dados:
MEDLINE
Assunto principal:
Biologia Computacional
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2011
Tipo de documento:
Article