title: Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.
abstract: BACKGROUND
  The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors.
  
  
  RESULTS
  In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property.
  
  
  CONCLUSIONS
  The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with a low cognitive effort in contrast with the alternative of using an ad-hoc manual analysis of the selected descriptors. Graphical abstractVIDEAN allows the visual analysis of candidate subsets of descriptors for QSAR/QSPR. In the two panels on the top, users can interactively explore numerical correlations as well as co-occurrences in the candidate subsets through two interactive graphs.
authors: Departamento de Ciencias e Ingeniería de la Computación, Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Instituto de Ciencias e Ingeniería de la Computación (ICIC), Universidad Nacional del Sur, Av. Alem 1253, 8000 Bahía Blanca, Argentina.
authors: María Jimena
authors: MJ
authors: Martínez
authors: Departamento de Ciencias e Ingeniería de la Computación, Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Instituto de Ciencias e Ingeniería de la Computación (ICIC), Universidad Nacional del Sur, Av. Alem 1253, 8000 Bahía Blanca, Argentina.
authors: Ignacio
authors: I
authors: Ponzoni
authors: Planta Piloto de Ingeniería Química (PLAPIQUI)-UNS-CONICET, Co., La Carrindanga km.7, CC 717 Bahía Blanca, Argentina.
authors: Mónica F
authors: MF
authors: Díaz
authors: Facultad de Ingeniería y Tecnologías, Universidad Católica del Uruguay, Av. 8 de Octubre 2801, CC 11300 Montevideo, Uruguay.
authors: Gustavo E
authors: GE
authors: Vazquez
authors: Faculty of Computer Science, Dalhousie University, 6050 University Av., Halifax, Canada.
authors: Axel J
authors: AJ
authors: Soto
keywords: Cheminformatics; Feature selection; QSAR; Visual analytics