Comparative Study of Classification Algorithms Using Molecular Descriptors in Toxicological DataBases

Authors:
Max Pereira;Vítor Santos Costa;Rui Camacho;Nuno A. Fonseca;Carlos Simões;Rui M. Brito
Affiliations:
LIAAD-INESC Porto LA & FEUP, Universidade do Porto, Porto, Portugal 4200-465;CRACS-INESC Porto LA, Universidade do Porto, Porto, Portugal 4169-007;LIAAD-INESC Porto LA & FEUP, Universidade do Porto, Porto, Portugal 4200-465;Instituto de Biologia Molecular e Celular (IBMC), Universidade do Porto, Porto, Portugal 4150-180 and CRACS-INESC Porto LA, Universidade do Porto, Porto, Portugal 4169-007;Chemistry Department, Faculty of Science and Technology and Center for Neuroscience and Cell Biology, University of Coimbra, Portugal;Chemistry Department, Faculty of Science and Technology and Center for Neuroscience and Cell Biology, University of Coimbra, Portugal
Venue:
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
Year:
2009

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Abstract

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 1D and 2D molecular descriptors. The results indicate that: i) Machine Learning algorithms can effectively use 1D molecular descriptors to construct accurate and simple models; ii) extending the set of descriptors to include 2D descriptors improve the accuracy of the models.