Interactive Visual Analysis Methodology for Improving Descriptor Selection in QSPR: First Steps (2 pag)

MARÍA JIMENA MARTÍNEZ; FIORELLA CRAVERO; GUSTAVO E. VAZQUEZ; MÓNICA. F. DIAZ; AXEL J. SOTO; IGNACIO PONZONI

San Carlos de Bariloche

Congreso; V. Congreso Argentino de Bioinformática y Biología Computacional (5CAB2C); 2014

AB2C2 y el Instituto de Energía y Desarrollo Sustentable (IEDS), Centro Atómico Bariloche (CNEA).

The design of QSAR/QSPR models requires dealing with several problems. One of them is the selection of the most relevant set of molecular descriptors for the property or activity that is intended to be modeled. One central point in this task is how we can involve the domain expert (e.g. a chemist), so that he can incorporate his knowledge and expertise during the feature selection process [1]. In this context, strategies based on dynamic visual analysis can be useful. The main idea behind visual analytics approaches is to merge the computational capacity of statistical and machine learning methods with the human natural ability of identifying patterns in visualizations. Therefore, by allowing some form of interaction in the visualizations, users can explore the data and provide feedback to the method, and/or use the tool to arrive at more informative decisions. In this work we report our first experiences in the design of a methodology, which combines statistical methods with interactive visualizations, in order to address the problem of molecular descriptor selection.