Abstract:BackgroundAnalysis and visualization of large collections of molecules is one of the most frequent challenges cheminformatics experts in pharmaceutical industry are facing. Various sophisticated methods are available to perform this task, including clustering, dimensionality reduction or scaffold frequency analysis. In any case, however, viewing and analyzing large tables with molecular structures is necessary. We present a new visualization technique, providing basic information about the composition of molec… Show more
“…This shows that when analyzing functionalities in large chemical databases it is not sufficient to limit ourselves to a predefined list of patterns, but it is necessary to identify all FGs.Fig. 4Example of some exotic functional groups identified displayed as molecule cloud [15]
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BackgroundThe concept of functional groups forms a basis of organic chemistry, medicinal chemistry, toxicity assessment, spectroscopy and also chemical nomenclature. All current software systems to identify functional groups are based on a predefined list of substructures. We are not aware of any program that can identify all functional groups in a molecule automatically. The algorithm presented in this article is an attempt to solve this scientific challenge.ResultsAn algorithm to identify functional groups in a molecule based on iterative marching through its atoms is described. The procedure is illustrated by extracting functional groups from the bioactive portion of the ChEMBL database, resulting in identification of 3080 unique functional groups.ConclusionsA new algorithm to identify all functional groups in organic molecules is presented. The algorithm is relatively simple and full details with examples are provided, therefore implementation in any cheminformatics toolkit should be relatively easy. The new method allows the analysis of functional groups in large chemical databases in a way that was not possible using previous approaches.Graphical abstract.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0225-z) contains supplementary material, which is available to authorized users.
“…This shows that when analyzing functionalities in large chemical databases it is not sufficient to limit ourselves to a predefined list of patterns, but it is necessary to identify all FGs.Fig. 4Example of some exotic functional groups identified displayed as molecule cloud [15]
…”
BackgroundThe concept of functional groups forms a basis of organic chemistry, medicinal chemistry, toxicity assessment, spectroscopy and also chemical nomenclature. All current software systems to identify functional groups are based on a predefined list of substructures. We are not aware of any program that can identify all functional groups in a molecule automatically. The algorithm presented in this article is an attempt to solve this scientific challenge.ResultsAn algorithm to identify functional groups in a molecule based on iterative marching through its atoms is described. The procedure is illustrated by extracting functional groups from the bioactive portion of the ChEMBL database, resulting in identification of 3080 unique functional groups.ConclusionsA new algorithm to identify all functional groups in organic molecules is presented. The algorithm is relatively simple and full details with examples are provided, therefore implementation in any cheminformatics toolkit should be relatively easy. The new method allows the analysis of functional groups in large chemical databases in a way that was not possible using previous approaches.Graphical abstract.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0225-z) contains supplementary material, which is available to authorized users.
“…Rather, different visualization techniques have also been introduced to generalize chemical space display including, for example, similarity-based compound networks [12] and molecular layout algorithms [13] for smaller data sets, projections from high-dimensional descriptors spaces based on principal component analysis for large (or very large) data sets [14,15], and generative topographic mapping (GTM) [16]. GTM was designed to project from high-dimensional feature spaces onto latent 2D space representations in which points (nodes) correspond to normal probability distributions derived from the original data space that determine the mapping of compounds to the latent space.…”
Compound optimization generally requires considering multiple properties in concert and reaching a balance between them. Computationally, this process can be supported by multi-objective optimization methods that produce numerical solutions to an optimization task. Since a variety of comparable multi-property solutions are usually obtained further prioritization is required. However, the underlying multi-dimensional property spaces are typically complex and difficult to rationalize. Herein, an approach is introduced to visualize multi-property landscapes by adapting the concepts of star and parallel coordinates from computer graphics. The visualization method is designed to complement multi-objective compound optimization. We show that visualization makes it possible to further distinguish between numerically equivalent optimization solutions and helps to select drug-like compounds from multi-dimensional property spaces. The methodology is intuitive, applicable to a wide range of chemical optimization problems, and made freely available to the scientific community.
“…Visualization of (quantitative) structure-activity relationship ((Q)SAR) information in chemical datasets is a very active field of research in cheminformatics [ 1 ]-[ 8 ]. Many approaches are being developed that help to understand existing correlations between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects.…”
BackgroundSound statistical validation is important to evaluate and compare the overall performance of (Q)SAR models. However, classical validation does not support the user in better understanding the properties of the model or the underlying data. Even though, a number of visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allow the investigation of model validation results are still lacking.ResultsWe propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. The approach applies the 3D viewer CheS-Mapper, an open-source application for the exploration of small molecules in virtual 3D space. The present work describes the new functionalities in CheS-Mapper 2.0, that facilitate the analysis of (Q)SAR information and allows the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. The approach is generic: It is model-independent and can handle physico-chemical and structural input features as well as quantitative and qualitative endpoints.ConclusionsVisual validation with CheS-Mapper enables analyzing (Q)SAR information in the data and indicates how this information is employed by the (Q)SAR model. It reveals, if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.Graphical abstractComparing actual and predicted activity values with CheS-Mapper.
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