A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents

• Type: Chapter or Part of a Book
• Date: 2014

Authors

Speck Planche, A (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Book

Title: Artificial Neural Networks: Second Edition

Pages: 45-64

ISBN: 9781493922390; 9781493922383

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00A-19H

DOI: 10.1007/978-1-4939-2239-0_4

Abstract (EN): Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/ vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions. © Springer Science+Business Media New York 2015. All rights are reserved.

Language: English

Type (Professor's evaluation): Scientific