Evolution of Graph Theory-Based QSAR Methods and their Applications to the Search for New Antibacterial Agents

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2013

Autores

Alejandro Speck Planche (Autor) Outra; Cordeiro, MNDS (Autor) FCUP

Revista

Título: Current Topics in Medicinal Chemistry

Vol. 13

Páginas: 3101-3117

ISSN: 1568-0266

Editora: Bentham

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Classificação Científica

FOS: Ciências médicas e da saúde > Medicina básica

Outras Informações

ID Authenticus: P-008-K6J

DOI: 10.2174/15680266112126660221

Abstract (EN): Resistance of bacteria to current antibiotics has increased worldwide, being one of the leading unresolved situations in public health. Due to negligence regarding the treatment of community-acquired diseases, even healthcare facilities have been highly impacted by an emerging problem: nosocomial infections. Moreover, infectious diseases, including nosocomial infections, have been found to depend on multiple pathogenicity factors, confirming the need to discover of multi-target antibacterial agents. Drug discovery is a very complex, expensive, and time-consuming process. In this sense, Quantitative Structure-Activity Relationships (QSAR) methods have become complementary tools for medicinal chemistry, permitting the efficient screening of potential drugs, and consequently, rationalizing the organic synthesis as well as the biological evaluation of compounds. In the consolidation of QSAR methods as important components of chemoinformatics, the use of mathematical chemistry, and more specifically, the use of graph-theoretical approaches has played a vital role. Here, we focus our attention on the evolution of QSAR methods, citing the most relevant works devoted to the development of promising graph-theoretical approaches in the last 8 years, and their applications to the prediction of antibacterial activities of chemicals against pathogens causing both community-acquired and nosocomial infections.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17