Multitasking models for quantitative structure-biological effect relationships: current status and future perspectives to speed up drug discovery

• Type: Article in International Scientific Journal
• Date: 2015

Authors

A. Speck-Planche (Author) Other; Maria Natália Cordeiro (Author) Other

Journal

Title: Expert Opinion on Drug Discovery

Vol. 10 No. 3

Pages: 245-256

ISSN: 1746-0441

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 44 Citations

ISI Web of Science

Scopus - 49 Citations

Scientific classification

FOS: Medical and Health sciences > Health sciences

CORDIS: Health sciences > Pharmacological sciences > Pharmacy

Other information

Authenticus ID: P-00A-79W

DOI: 10.1517/17460441.2015.1006195

Abstract (EN): Introduction: Drug discovery is the process of designing new candidate medications for the treatment of diseases. Over many years, drugs have been identified serendipitously. Nowadays, chemoinformatics has emerged as a great ally, helping to rationalize drug discovery. In this sense, quantitative structure-activity relationships (QSAR) models have become complementary tools, permitting the efficient virtual screening for a diverse number of pharmacological profiles. Despite the applications of current QSAR models in the search for new drug candidates, many aspects remain unresolved. To date, classical QSAR models are able to predict only one type of biological effect (activity, toxicity, etc.) against only one type of generic target. Areas covered: The present review discusses innovative and evolved QSAR models, which are focused on multitasking quantitative structure-biological effect relationships (mtk-QSBER). Such models can integrate multiple kinds of chemical and biological data, allowing the simultaneous prediction of pharmacological activities, toxicities and/or other safety profiles. Expert opinion: The authors strongly believe, given the potential of mtk-QSBER models to simultaneously predict the dissimilar biological effects of chemicals, that they have much value as in silico tools for drug discovery. Indeed, these models can speed up the search for efficacious drugs in a number of areas, including fragment-based drug discovery and drug repurposing.

Language: English

Type (Professor's evaluation): Scientific