A Binary Ant Colony Optimization Classifier for Molecular Activities

作者：Hammann Felix^*; Suenderhauf Claudia; Huwyler Joerg

来源：Journal of Chemical Information and Modeling, 2011, 51(10): 2690-2696.

DOI：10.1021/ci200186m

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.

• 出版日期2011-10