Journal ArticleDOI
Quantitative structure-retention relationships models for prediction of high performance liquid chromatography retention time of small molecules: endogenous metabolites and banned compounds.
Krzysztof Goryński,Barbara Bojko,Alicja Nowaczyk,Adam Buciński,Janusz Pawliszyn,Roman Kaliszan +5 more
TLDR
This paper provides a practical and effective method for analytical chemists working with LC/HRMS platforms to improve predictive confidence of studies that seek to identify small molecules.About:
This article is published in Analytica Chimica Acta.The article was published on 2013-10-03. It has received 82 citations till now.read more
Citations
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Journal ArticleDOI
Annotation: A Computational Solution for Streamlining Metabolomics Analysis.
TL;DR: The state-of-the-art strategies for computational annotation including: peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; annotation using ion adduction and mass distance among ion peaks; and incorporation of biological knowledge such as biotransformations or pathways are examined.
Journal ArticleDOI
PredRet: prediction of retention time by direct mapping between multiple chromatographic systems.
TL;DR: PredRet is presented; the first tool that makes community sharing of RT information possible across laboratories and chromatographic systems (CSs) and can thus prioritize which isomers to target for further characterization and potentially exclude some structures completely.
Journal ArticleDOI
Development and application of retention time prediction models in the suspect and non-target screening of emerging contaminants.
TL;DR: In this paper, a novel comprehensive workflow was developed to study the tR behavior of large groups of emerging contaminants using Quantitative Structure-Retention Relationships (QSRR), and validated models for predicting tR in HILIC/RPLC-HRMS platforms to facilitate identification of new emerging contaminants by suspect and non-target HRMS screening.
Journal ArticleDOI
The METLIN small molecule dataset for machine learning-based retention time prediction
Xavier Domingo-Almenara,Xavier Domingo-Almenara,Carlos Guijas,Elizabeth Billings,J. Rafael Montenegro-Burke,Winnie Uritboonthai,Aries E. Aisporna,Emily I. Chen,H. Paul Benton,Gary Siuzdak +9 more
TL;DR: The authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification and anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.
Journal ArticleDOI
Quantitative Structure-Retention Relationship Models To Support Nontarget High-Resolution Mass Spectrometric Screening of Emerging Contaminants in Environmental Samples.
TL;DR: A comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants.
References
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Journal ArticleDOI
ZINC - A Free Database of Commercially Available Compounds for Virtual Screening
John J. Irwin,Brian K. Shoichet +1 more
TL;DR: This paper has prepared a library of 727,842 molecules, each with 3D structure, using catalogs of compounds from vendors, and hopes that this database will bring virtual screening libraries to a wide community of structural biologists and medicinal chemists.
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Handbook of Molecular Descriptors
TL;DR: This Users guide notations acronyms list of molecular descriptors contains abbreviations for molecular descriptor values that are useful for counting and topological descriptors calculation.
Journal ArticleDOI
HMDB: a knowledgebase for the human metabolome
David S. Wishart,Craig Knox,An Chi Guo,Roman Eisner,Nelson Young,Bijaya Gautam,David Hau,Nick Psychogios,Edison Dong,Souhaila Bouatra,Rupasri Mandal,Igor Sinelnikov,Jianguo Xia,Leslie Jia,Joseph A. Cruz,Emilia L. Lim,Constance A. Sobsey,Savita Shrivastava,Paul Huang,Philip Liu,Lydia Fang,Jun Peng,Ryan Fradette,Dean Cheng,Dan Tzur,Melisa Clements,Avalyn Lewis,Andrea De Souza,Azaret Zuniga,Margot Dawe,Yeping Xiong,Derrick L. J. Clive,Russell Greiner,Alsu Nazyrova,Rustem Shaykhutdinov,Liang Li,Hans J. Vogel,Ian J. Forsythe +37 more
TL;DR: The most recent release of HMDB has been significantly expanded and enhanced over the previous release, with the number of fully annotated metabolite entries growing from 2180 to more than 6800, a 300% increase.
Journal ArticleDOI
Principles of QSAR models validation: internal and external
TL;DR: Evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes.
Journal ArticleDOI
Best Practices for QSAR Model Development, Validation, and Exploitation.
TL;DR: Most critical QSAR modeling routines that are regarded as best practices in the field are examined, including procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries.