Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface

Weidlich, Iwona E.; Pevzner, Yuri; Miller, Benjamin; Filippov, Igor V.; Woodcock, H. Lee; Brooks, Bernard R.

doi:10.1002/jcc.23765

Cited by 5 publications

(5 citation statements)

References 40 publications

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“…To our knowledge, there are two web tools available for screening small molecules. MolClass makes use of several machine learning algorithms and generates computational models from small molecule data sets using structural features identified in hit and non-hit molecules [ 38 ], and CHARMMing (Chemistry at Harvard Macromolecular Mechanics Interface and Graphics) performs quantitative structure activity relationship modeling using fifteen different machine learning algorithms [ 39 ]. Both tools benefit from the PubChem bioassay data sets.…”

Section: Resultsmentioning

confidence: 99%

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

2015

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Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/.

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Section: Resultsmentioning

confidence: 99%

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

2015

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“…The recently developed online tools REACH across , and the Chemical In Vitro – In Vivo Profiling portal (CIIPro) provide new methods to extract data from the REACH and PubChem databases automatically. Additionally, online tools such as Chembench and the Chemistry at Harvard Macromolecular Mechanics web-user interface (CHARMMing) are available to streamline the development and distribution of curated toxicity data and QSAR models.…”

Section: Data-driven Computational Toxicology Modelingmentioning

confidence: 99%

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

Ciallella

Zhu

2019

Chem. Res. Toxicol.

157

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In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.

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“…CHARMMing [http://www.charmming.org]: This web site was previously developed as a Web‐based front‐end to the CHARMM molecular simulation package . Since 2015 it has been extended with tools to develop (Q)SAR models using several machine learning algorithms for both regression and classification tasks . The web site (that requires free registration) uses 2048‐bit Morgan fingerprints calculated using RDkit [http://www.rdkit.org/docs].…”

Section: (Q)sar Models In Integrated Modeling Environmentsmentioning

confidence: 99%

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

Tetko

Maran

Tropsha

2016

Molecular Informatics

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Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.

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Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface

Cited by 5 publications

References 40 publications

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

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

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