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      Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods

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          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 abstract

          VIDEAN 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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13321-015-0092-4) contains supplementary material, which is available to authorized users.

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          Most cited references33

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          Descriptor selection methods in quantitative structure-activity relationship studies: a review study.

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            Mechanical Properties of Solid Polymers

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              Current Mathematical Methods Used in QSAR/QSPR Studies

              This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.
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                Author and article information

                Contributors
                mjma@cs.uns.edu.ar
                ip@cs.uns.edu.ar
                mdiaz@plapiqui.edu.ar
                gustavo.vazquez@ucu.edu.uy
                soto@cs.dal.ca
                Journal
                J Cheminform
                J Cheminform
                Journal of Cheminformatics
                Springer International Publishing (Cham )
                1758-2946
                19 August 2015
                19 August 2015
                2015
                : 7
                : 39
                Affiliations
                [ ]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
                [ ]Planta Piloto de Ingeniería Química (PLAPIQUI)-UNS-CONICET, Co., La Carrindanga km.7, CC 717 Bahía Blanca, Argentina
                [ ]Facultad de Ingeniería y Tecnologías, Universidad Católica del Uruguay, Av. 8 de Octubre 2801, CC 11300 Montevideo, Uruguay
                [ ]Faculty of Computer Science, Dalhousie University, 6050 University Av., Halifax, Canada
                Article
                92
                10.1186/s13321-015-0092-4
                4540751
                3a7620c0-47e5-4d46-b7a5-189805f9eeb8
                © Martinez et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 January 2015
                : 30 July 2015
                Categories
                Software
                Custom metadata
                © The Author(s) 2015

                Chemoinformatics
                feature selection,visual analytics,qsar,cheminformatics
                Chemoinformatics
                feature selection, visual analytics, qsar, cheminformatics

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