Your browser doesn't support javascript.
loading
In Silico Structure Predictions for Non-targeted Analysis: From Physicochemical Properties to Molecular Structures.
Abrahamsson, Dimitri; Siddharth, Adi; Young, Thomas M; Sirota, Marina; Park, June-Soo; Martin, Jonathan W; Woodruff, Tracey J.
Afiliación
  • Abrahamsson D; Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, California 94143, United States.
  • Siddharth A; Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, California 94143, United States.
  • Young TM; Department of Civil and Environmental Engineering, University of California, Davis, One Shields Avenue, Davis, California 95616, United States.
  • Sirota M; Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California 94158, United States.
  • Park JS; Department of Pediatrics, University of California San Francisco, San Francisco, California 94158, United States.
  • Martin JW; Department of Obstetrics, Gynecology and Reproductive Sciences, Program on Reproductive Health and the Environment, University of California San Francisco, San Francisco, California 94143, United States.
  • Woodruff TJ; California Environmental Protection Agency, Department of Toxic Substances Control, Environmental Chemistry Laboratory, Berkeley, California 94710, United States.
J Am Soc Mass Spectrom ; 33(7): 1134-1147, 2022 Jul 06.
Article en En | MEDLINE | ID: mdl-35649165
While important advances have been made in high-resolution mass spectrometry (HRMS) and its applications in non-targeted analysis (NTA), the number of identified compounds in biological and environmental samples often does not exceed 5% of the detected chemical features. Our aim was to develop a computational pipeline that leverages data from HRMS but also incorporates physicochemical properties (equilibrium partition ratios between organic solvents and water; Ksolvent-water) and can propose molecular structures for detected chemical features. As these physicochemical properties are often sufficiently different across isomers, when put together, they can form a unique profile for each isomer, which we describe as the "physicochemical fingerprint". In our study, we used a comprehensive database of compounds that have been previously reported in human blood and collected their Ksolvent-water values for 129 partitioning systems. We used RDKit to calculate the number of RDKit fragments and the number of RDKit bits per molecule. We then developed and trained an artificial neural network, which used as an input the physicochemical fingerprint of a chemical feature and predicted the number and types of RDKit fragments and RDKit bits present in that structure. These were then used to search the database and propose chemical structures. The average success rate of predicting the right chemical structure ranged from 60 to 86% for the training set and from 48 to 81% for the testing set. These observations suggest that physicochemical fingerprints can assist in the identification of compounds with NTA and substantially improve the number of identified compounds.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Agua Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Am Soc Mass Spectrom Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Agua Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Am Soc Mass Spectrom Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos