title: A binary ant colony optimization classifier for molecular activities.
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.
authors.affiliation: Division of Pharmaceutical Technology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50 4056, Basel, Switzerland. felix.hammann@unibas.ch
authors.forename: Felix
authors.initials: F
authors.lastname: Hammann
authors.forename: Claudia
authors.initials: C
authors.lastname: Suenderhauf
authors.forename: Jörg
authors.initials: J
authors.lastname: Huwyler
mesh_terms: D000465:Algorithms; D000981:Antiprotozoal Agents / Q000737:chemistry / Q000494:pharmacology; D019295:Computational Biology / Q000379:methods*; D010963:Plasmodium falciparum / Q000187:drug effects; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000981:Antiprotozoal Agents