Predicting Kováts Retention Indices Using Graph Neural Networks

DeepReI: Deep learning-based gas chromatographic retention index predictor

(2020) Tomáš Vrzal et al.   ANALYTICA CHIMICA ACTA

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

A deep convolutional neural network for the estimation of gas chromatographic retention indices

(2019) Dmitriy D. Matyushin et al.   JOURNAL OF CHROMATOGRAPHY A

Methodological Approaches to the Calculation and Prediction of Retention Indices in Capillary Gas Chromatography

(2018) A. K. Zhokhov et al.   JOURNAL OF ANALYTICAL CHEMISTRY

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Constant size descriptors for accurate machine learning models of molecular properties

(2018) Christopher R. Collins et al.   JOURNAL OF CHEMICAL PHYSICS

Improving Accuracy and Confidence of Chemical Identification by GC/VUV-MS: Parallel GC, VUV, and MS Library Searches

(2018) Ian G. M Anthony et al.   ANALYTICAL CHEMISTRY

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

QSPR analysis for the retention index of flavors and fragrances on a OV-101 column

(2015) Cristian Rojas et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Quantitative structure–property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase

(2015) Cristian Rojas et al.   JOURNAL OF CHROMATOGRAPHY A

iMatch2: Compound identification using retention index for analysis of gas chromatography–mass spectrometry data

(2014) Imhoi Koo et al.   JOURNAL OF CHROMATOGRAPHY A

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

(2013) Jun Yan et al.   JOURNAL OF SEPARATION SCIENCE

Consensus Structure Elucidation Combining GC/EI-MS, Structure Generation, and Calculated Properties

(2012) Emma L. Schymanski et al.   ANALYTICAL CHEMISTRY

Use of Large Retention Index Database for Filtering of GC–MS False Positive Identifications of Compounds

(2012) V. I. Babushok et al.   CHROMATOGRAPHIA

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

(2012) Lars Ruddigkeit et al.   Journal of Chemical Information and Modeling

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Automated Strategies To Identify Compounds on the Basis of GC/EI-MS and Calculated Properties

(2011) Emma L. Schymanski et al.   ANALYTICAL CHEMISTRY

ApplyingIn-SilicoRetention Index and Mass Spectra Matching for Identification of Unknown Metabolites in Accurate Mass GC-TOF Mass Spectrometry

(2011) Sangeeta Kumari et al.   ANALYTICAL CHEMISTRY

iMatch: A retention index tool for analysis of gas chromatography–mass spectrometry data

(2011) Jun Zhang et al.   JOURNAL OF CHROMATOGRAPHY A

Retention Indices for Frequently Reported Compounds of Plant Essential Oils

(2011) V. I. Babushok et al.   JOURNAL OF PHYSICAL AND CHEMICAL REFERENCE DATA

Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

(2010) Alan R. Katritzky et al.   CHEMICAL REVIEWS

Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index

(2009) V. V. Mihaleva et al.   BIOINFORMATICS

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

(2009) Lorenz C. Blum et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY