title: Prediction of bioactive peptides using artificial neural networks.
abstract: Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.
authors.affiliation: Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Dr. Aiguader 88, 08003, Barcelona, Spain, david.andreu@upf.edu.
authors.forename: David
authors.initials: D
authors.lastname: Andreu
authors.forename: Marc
authors.initials: M
authors.lastname: Torrent
mesh_terms: D000890:Anti-Infective Agents / Q000737:chemistry / Q000302:isolation & purification* / Q000494:pharmacology; D019295:Computational Biology / Q000379:methods*; D062313:Databases, Pharmaceutical; D015195:Drug Design; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry / Q000302:isolation & purification* / Q000494:pharmacology; D012984:Software
chemical_list: D000890:Anti-Infective Agents; D010455:Peptides