J Chem Inf Model - A binary ant colony optimization classifier for molecular activities.

Tópicos

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Resumo

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.

Resumo Limpo

chemic fingerprint encod presenc absenc molecular featur avail mani larg databas use variat ant coloni optim aco paradigm describ binari classifi base featur select fingerprint discuss algorithm possibl crossvalid procedur realworld exampl use algorithm analyz plasmodium falciparum inhibit assay contrast perform machin learn paradigm use today decis tree induct random forest support vector machin artifici neural network algorithm match establish paradigm predict power yet suppli medicin chemist basic research easili interpret result furthermor model generat paradigm easi implement can complement virtual screen addit exploit precalcul fingerprint inform

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