Graph convolutional networks for improved prediction and interpretability of chromatographic retention data
Machine learning is a popular technique to predict the retention times of molecules based
on descriptors. Descriptors and associated labels (eg, retention times) of a set of molecules …
on descriptors. Descriptors and associated labels (eg, retention times) of a set of molecules …
Prediction of liquid chromatographic retention time with graph neural networks to assist in small molecule identification
The predicted liquid chromatographic retention times (RTs) of small molecules are not
accurate enough for wide adoption in structural identification. In this study, we used the …
accurate enough for wide adoption in structural identification. In this study, we used the …
Gas chromatographic retention index prediction using multimodal machine learning
DD Matyushin, AK Buryak - Ieee Access, 2020 - ieeexplore.ieee.org
Gas chromatography is a widely used method in analytical chemistry and metabolomics.
Using gas chromatography, vaporizable compounds can be separated for their further …
Using gas chromatography, vaporizable compounds can be separated for their further …
Are learned molecular representations ready for prime time?
Advancements in neural machinery have led to a wide range of algorithmic solutions for
molecular property prediction. Two classes of models in particular have yielded promising …
molecular property prediction. Two classes of models in particular have yielded promising …
Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns
D Vik, D Pii, C Mudaliar, M Nørregaard-Madsen… - Scientific Reports, 2024 - nature.com
This study explores how machine-learning can be used to predict chromatographic retention
times (RT) for the analysis of small molecules, with the objective of identifying a machine …
times (RT) for the analysis of small molecules, with the objective of identifying a machine …
Analyzing learned molecular representations for property prediction
Advancements in neural machinery have led to a wide range of algorithmic solutions for
molecular property prediction. Two classes of models in particular have yielded promising …
molecular property prediction. Two classes of models in particular have yielded promising …
Graph Neural Tree: A novel and interpretable deep learning-based framework for accurate molecular property predictions
H Zhan, X Zhu, Z Qiao, J Hu - Analytica Chimica Acta, 2023 - Elsevier
Determining various properties of molecules is a critical step in drug discovery. Recently,
with the improvement of large heterogeneous datasets and the development of deep …
with the improvement of large heterogeneous datasets and the development of deep …
Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning
The combination of retention time (RT), accurate mass and tandem mass spectra can
improve the structural annotation in untargeted metabolomics. However, the incorporation of …
improve the structural annotation in untargeted metabolomics. However, the incorporation of …
Predicting kovats retention indices using graph neural networks
The Kováts retention index is a dimensionless quantity that characterizes the rate at which a
compound is processed through a gas chromatography column. This quantity is …
compound is processed through a gas chromatography column. This quantity is …
DeepReI: Deep learning-based gas chromatographic retention index predictor
T Vrzal, M Malečková, J Olšovská - Analytica Chimica Acta, 2021 - Elsevier
Retention index in gas chromatographic analyses is an essential tool for appropriate analyte
identification. Currently, many libraries providing retention indices for a huge number of …
identification. Currently, many libraries providing retention indices for a huge number of …