Comparative study of classification algorithms using molecular descriptors in toxicological databases

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Max Pereira (Autor) Outra; Vítor Santos Costa (Autor) FCUP; Rui Camacho (Autor) FEUP; Nuno A. Fonseca (Autor) Outra; Carlos Simões (Autor) Outra; Rui M. M. Brito (Autor) Outra

Ata de Conferência Internacional

Título: ADVANCES IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, PROCEEDINGS

Páginas: 121-132

4th Brazilian Symposium on Bioinformatics (BSB 2009)

Porto Alegre, BRAZIL, JUL 29-31, 2009

Indexação

ISI Web of Knowledge - 7 Citações

Scopus - 7 Citações

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores ; Ciências da Saúde > Ciências farmacológicas

Outras Informações

ID Authenticus: P-003-NXG

DOI: 10.1007/978-3-642-03223-3_11

Abstract (EN): The rational development of new drugs is a complex and expensive process, comprising several steps. Typically, it starts by screening databases of small organic molecules for chemical structures with potential of binding to a target receptor and prioritizing the most promising ones. Only a few of these will be selected for biological evaluation and further refinement through chemical synthesis. Despite the accumulated knowledge by pharmaceutical companies that continually improve the process of finding new drugs, a myriad of factors affect the activity of putative candidate molecules in vivo and the propensity for causing adverse and toxic effects is recognized as the major hurdle behind the current "target-rich, lead-poor" scenario. In this study we evaluate the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using ID and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use ID molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12