Abstract:Background
Molecular descriptors and fingerprints have been routinely used in QSAR/SAR analysis, virtual drug screening, compound search/ranking, drug ADME/T prediction and other drug discovery processes. Since the calculation of such quantitative representations of molecules may require substantial computational skills and efforts, several tools have been previously developed to make an attempt to ease the process. However, there are still several hurdles for users to overcome to fully harness the power of th… Show more
“…As an easy-to-use web service, ChemSAR supports commonly-used file formats for data exchange between the server-side and the client-side. Specifically, simplified molecular input line entry specification (SMILES) and Structure Data Format (SDF) are acceptable molecular file formats (or users can convert their files into these two formats using OpenBable [37] or ChemCONV [49]). The modeling results will be presented as HTML web pages, but users can download the results in SDF, CSV, PNG or PDF format (see Table 1 for details).Fig.…”
BackgroundIn recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers.ResultsThis work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files.ConclusionChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract.
Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users.
“…As an easy-to-use web service, ChemSAR supports commonly-used file formats for data exchange between the server-side and the client-side. Specifically, simplified molecular input line entry specification (SMILES) and Structure Data Format (SDF) are acceptable molecular file formats (or users can convert their files into these two formats using OpenBable [37] or ChemCONV [49]). The modeling results will be presented as HTML web pages, but users can download the results in SDF, CSV, PNG or PDF format (see Table 1 for details).Fig.…”
BackgroundIn recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers.ResultsThis work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files.ConclusionChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.Graphical abstract.
Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users.
“…This data set consists of 1290 diverse compounds and has been analyzed by several groups. Six types of molecular descriptors (252 molecular descriptors) were calculated with the ChemDes, BioTriangle, Rcpi, and ChemoPy descriptor calculation program, developed by our group. Two pretreatments were performed to delete some uninformative descriptors before further analysis: (1) delete the descriptors whose variance is 0 or approaches 0, and (2) if the correlation coefficient between 2 descriptors is higher than 0.95, only one was reserved.…”
In QSAR/QSPR modeling, building an accurate partial least squares (PLS) model usually involves descriptor selection, outlier detection, applicability domain assessment, nonlinear relationship, and model stability problems. In the present study, we presented an ensemble PLS (EnPLS) method for solving these modeling tasks under a unified methodology framework. EnPLS aims at developing a consistent algorithmic framework by means of the idea of ensemble learning and statistical distribution. The approach exploits the fact that the distribution of PLS model coefficients provides a mechanism for ranking and interpreting the effects of variables, whereas the distribution of prediction errors provides a mechanism for differentiating the outliers from normal samples and assessing the applicability domain of models. The use of statistics of these distributions, namely, mean/median value and standard deviation, inherently provides a feasible way to effectively describe the information contained by the original samples. Furthermore, ensemble modeling and prediction based on several cross-predictive PLS models could effectively improve the model prediction performance and increase the model stability to a certain extent. The aqueous solubility data are used to demonstrate the ability of our proposed EnPLS method in solving various modeling tasks such as descriptor selection, outlier detection, applicability domain assessment, performance improvement, and model stability. Finally, a freely available R package implementing EnPLS is developed to facilitate the use of chemists and pharmacologists. The R package is freely available at https://github.com/wind22zhu/enpls1.2. KEYWORDS applicability domain assessment, ensemble learning, outlier detection, partial least squares (PLS), QSAR/ QSPR, variable selection 1 | INTRODUCTIONChemometrics aims at solving chemical problems using statistical or informatics methods. There is a great deal of previous successful research in this filed, revolving around topics such as quantitative structure-activity relationships (QSARs), quantitative structure-property relationships (QSPRs), multivariate calibration, reaction design, and drug design. The QSAR/QSPR methodology aims at finding a model, which allows for correlating the activities to structures within a family of compounds.
“…The Tanimoto similarity index calculated between F0514-4011 and most of the published MET ligands shows that the molecule identified by our virtual screening campaign seems to be an innovative hit as all the Tanimoto values are low, ranging from 0.39 (with the pioneer inhibitor PHA-665752) to 0.12 (for norcantharidin) (Table S2). We have completed this quite elementary similarity search by using the ChemDes web server [68], which allows a large panel of similarity fingerprint types as well as fingerprints descriptors and similarity measures. Using this web server, we mined several databases collecting MET known inhibitors (such as the PDB or PubChem [69]), already in clinical trials (such as MDDR [70]), or described as putative inhibitors (such as in Life Chemical or sellekchem [71] providers).…”
By using an ensemble-docking strategy, we undertook a large-scale virtual screening campaign in order to identify new putative hits against the MET kinase target. Following a large molecular dynamics sampling of its conformational space, a set of 45 conformers of the kinase was retained as docking targets to take into account the flexibility of the binding site moieties. Our screening funnel started from about 80,000 chemical compounds to be tested in silico for their potential affinities towards the kinase binding site. The top 100 molecules selected—thanks to the molecular docking results—were further analyzed for their interactions, and 25 of the most promising ligands were tested for their ability to inhibit MET activity in cells. F0514-4011 compound was the most efficient and impaired this scattering response to HGF (Hepatocyte Growth Factor) with an IC 50 of 7.2 μ M. Interestingly, careful docking analysis of this molecule with MET suggests a possible conformation halfway between classical type-I and type-II MET inhibitors, with an additional region of interaction. This compound could therefore be an innovative seed to be repositioned from its initial antiviral purpose towards the field of MET inhibitors. Altogether, these results validate our ensemble docking strategy as a cost-effective functional method for drug development.
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