Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data

Kensert, Alexander; Bouwmeester, Robbin; Efthymiadis, Kyriakos; Broeck, Peter Van den; Desmet, Gert; Cabooter, Deirdre

doi:10.1021/acs.analchem.1c02988

Cited by 29 publications

(11 citation statements)

References 44 publications

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“…In the past years, a new type of neural network designed to interpret chemical systems and capture their distinct features was developed, fulfilling a need for a design that would be natural for a chemical structure and its environment. Graph convolutional networks − and similar architectures are now well-established in the field. Among them, neural networks that predict potential energy, called neural network potentials (NNPs), are of great importance.…”

Section: Introductionmentioning

confidence: 99%

Constructing Collective Variables Using Invariant Learned Representations

Šípka

Erlebach

Grajciar

2023

J. Chem. Theory Comput.

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On the time scales accessible to atomistic numerical modeling, chemical reactions are considered rare events. Therefore, the atomistic simulations are commonly biased along a lowdimensional representation of a chemical reaction in an atomic structure space, i.e., along the collective variables. However, suitable collective variables are often complicated to guess a priori. We propose a novel method of collective variable discovery based on dimensionality reduction of the atomic representation vectors. These linear-scaling and invariant representations can be either fixed (untrained) or learned by supervised training of the end-toend machine learning potential. The learned representations are expected to reflect not only the structural but also the energetic features of the system that are transferable to all of the reactive transformation covered by the machine learning potential. We demonstrate our approach on four high-barrier reactions ranging from a simple gas-phase hydrogen jump reaction to complex reactions in periodic models of industrially relevant heterogeneous catalysts. High data efficiency, automatized feature extraction, favorable scaling, and retention of inherent invariances are all properties that are expected to enable fast and largely automatic construction of suitable collective variables even in highly complex reactive scenarios such as reactive/catalytic transformations at solid−liquid interfaces.

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Section: Introductionmentioning

confidence: 99%

Constructing Collective Variables Using Invariant Learned Representations

Šípka

Erlebach

Grajciar

2023

J. Chem. Theory Comput.

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“…Machine learning approaches have been adopted to accurately predict the RT of a molecule in a data-driven manner. ,,− These approaches build a prediction model that uses the representation of a molecule as the input to predict the RT. The model learns from a training data set containing a number of molecules and their RT measurements according to a target chromatographic system.…”

Section: Introductionmentioning

confidence: 99%

Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network

Kwon,

Han

et al. 2023

Anal. Chem.

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Graph neural networks (GNNs) have shown remarkable performance in predicting the retention time (RT) for small molecules. However, the training data set for a particular target chromatographic system tends to exhibit scarcity, which poses a challenge because the experimental process for measuring RT is costly. To address this challenge, transfer learning has been used to leverage an abundant training data set from a related source task. In this study, we present an improved transfer learning method to better predict the RT of molecules for a target chromatographic system by learning from a small training data set with a pretrained GNN. We use a graph isomorphism network as the architecture of the GNN. The GNN is pretrained on the METLIN-SMRT data set and is then fine-tuned on the target training data set for a fixed number of training iterations using the limitedmemory Broyden−Fletcher−Goldfarb−Shanno optimizer with a learning rate decay. We demonstrate that the proposed method achieves superior predictive performance on various chromatographic systems compared with that of the existing transfer learning methods, especially when only a small training data set is available for use. A potential avenue for future research is to leverage multiple small training data sets from different chromatographic systems to further enhance the generalization performance.

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“…Several studies have predicted RT, ,− CCS values, ,− or both . Predictors of RT have been developed mainly to model RT data in reverse-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) with prediction accuracy between approximately ±1 to ±3 min (up to 22% of the chromatographic gradient length).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Retention Time and Collision Cross Section (CCS_H+, CCS_H–, and CCS_Na+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

Celma

Bade

Sancho³

et al. 2022

J. Chem. Inf. Model.

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Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R 2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R 2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R 2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

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Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data

Cited by 29 publications

References 44 publications

Constructing Collective Variables Using Invariant Learned Representations

Constructing Collective Variables Using Invariant Learned Representations

Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network

Prediction of Retention Time and Collision Cross Section (CCS_H+, CCS_H–, and CCS_Na+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

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Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data

Cited by 29 publications

References 44 publications

Constructing Collective Variables Using Invariant Learned Representations

Constructing Collective Variables Using Invariant Learned Representations

Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH–, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines

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Product

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Prediction of Retention Time and Collision Cross Section (CCS_H+, CCS_H–, and CCS_Na+) of Emerging Contaminants Using Multiple Adaptive Regression Splines