Robust automatic assignment of nuclear magnetic resonance spectra for small molecules

Abstract. In this document we describe a fully automatic assignment system for Nuclear Magnetic Resonance (NMR) for small molecules. This system has 3 main features: 1. it uses as input raw NMR data. Which means it should be able to extract from them the information that is useful while ignores the noise; 2. it assigns the signals to atoms in the structure, and associates to each assignment a confidence value, which is used to sort all possible solutions; 3. it does not depend on chemical shifts predictions. So it can use the connectivity information observed in 2D NMR spectra and integrals to perform an assignment(coupling constants are also a possibility, but were not explored in this work). However, the system can use chemical shifts if available.; 4. it can learn in an unsupervised fashion, the relation between configurations of atoms and chemical shifts while solving assignment problems, which allows the system to improve while working. Analogous to the way a human works. This system is completely open source, as well as the data used in this work.

• Autores: Castillo Robles, Andrés Mauricio
• Tipo de recurso: Doctoral thesis
• Fecha de publicación: 2015
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/55957
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/55957; http://bdigital.unal.edu.co/51491/
• Palabra clave: 61 Ciencias médicas; Medicina / Medicine and health; 62 Ingeniería y operaciones afines / Engineering; NMR; assignment; prediction; peak-picking; learning; analysis; molecule; spectra; hose; RMN; Asignación; Predicción; Similitud
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional