Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development

QSAR analysis of a set of previously synthesized phosphonium ionic liquids (PILs) tested against Gram-negative multidrug-resistant clinical isolate Acinetobacter baumannii was done using the Online Chemical Modeling Environment (OCHEM). To overcome the problem of overfitting due to descriptor selection, fivefold cross-validation with variable selection in each step of the model development was applied.The predictive ability of the classification models was tested by cross-validation, giving balanced accuracies (BA) of 76%-82%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA = 83%-89%). The models were applied to screen a virtual chemical library with expected activity of compounds against MDR Acinetobacter baumannii. The eighteen most promising compounds were identified, synthesized, and tested. Biological testing of compounds was performed using the disk diffusion method in Mueller-Hinton agar. All tested molecules demonstrated high anti-A. baumannii activity and different toxicity levels. The developed classification SAR models are freely available online at http://ochem.eu/artic le/113921 and could be used by scientists for design of new more effective antibiotics. K E Y W O R D SAcinetobacter baumanii, antibacterial activity, machine learning, OCHEM, phosphonium ionic liquids

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“…All compounds in this set were unique. The collected data are freely accessible under the CC‐BY license on the OCHEM (Tetko et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…The main goal of this study was to investigate the anti‐ A. baumanii activity of a number of PILs based on structure–activity relationship studies (Dearden, 2016) derived using computational models developed by the On‐line Chemical Database and Modeling Environment (OCHEM) (Tetko, Maran, & Tropsha, 2017).…”

Section: Introductionmentioning

confidence: 99%

In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii

et al. 2020

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“…The trend for future development of SAR models is towards making models publicly accessible on-line, interactive, and usable [78]. ChemSAR, to some extent, has made a step in this direction.…”

Section: Resultsmentioning

confidence: 99%

ChemSAR: an online pipelining platform for molecular SAR modeling

et al. 2017

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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.

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“…The recent overview of OCHEM functionality as well as other web-tools for development of online can be found elsewhere (Tetko et al, 2017).…”

Section: Ochem Databasementioning

confidence: 99%

Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform

Kovalishyn

Abramenko

Kopernyk

et al. 2018

Food and Chemical Toxicology

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Inorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q = 0.58-0.80 for regression models and balanced accuracies of 65-88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.

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Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development

Cited by 36 publications

References 86 publications

In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii

In silico and in vitro studies of a number PILs as new antibacterials against MDR clinical isolate Acinetobacter baumannii

ChemSAR: an online pipelining platform for molecular SAR modeling

Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform

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