Machine learning to predict retention time of small molecules in nano-HPLC

Osipenko, Sergey; Bashkirova, Inga; Sosnin, Sergey; Kovaleva, Oxana; Fedorov, Maxim V.; Nikolaev, Eugene; Kostyukevich, Yury

doi:10.1007/s00216-020-02905-0

Cited by 32 publications

(36 citation statements)

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“…Taking inspiration from [ 24 ], an additional binary feature was added to each molecule representation indicating whether the molecule is retained or not. Since in a real world application this information would not be available, this feature must also be predicted.…”

Section: Methodsmentioning

confidence: 99%

“…Gradient Boosting Machines (GBMs) have already been considered in state of the art methods for Retention Time (RT) prediction [ 24 ]. In this work, several GBMs were tested, using slightly different approaches for the hyperparameter search.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to the interest of comparing several GBMs, the use of different combinations of GBMs and tuning options was partially motivated by the need of having diversity in the predictions for building a good ensemble (see " Blending " Section). Specifically, we tested: XGBoost [ 34 ]: it is probably the most-commonly used GBM, and it was employed for Retention Time (RT) prediction in [ 24 ]. Bayesian search was applied on different regressors fed using fingerprints, descriptors and fingerprints+descriptors.…”

Section: Methodsmentioning

confidence: 99%

“…Hyperparameter search for the models was performed with the Tree-structured Parzen Estimator (TPE) algorithm [ 27 ], and a nested cross-validation was used in the evaluation. The best model was a Deep Neural Network (DNN) trained using molecular fingerprints, which improved the performance of the best previous models to predict Retention Time (RT) [ 24 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic metabolite annotation using retention time prediction and meta-learned projections

et al. 2022

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Retention time information is used for metabolite annotation in metabolomic experiments. But its usefulness is hindered by the availability of experimental retention time data in metabolomic databases, and by the lack of reproducibility between different chromatographic methods. Accurate prediction of retention time for a given chromatographic method would be a valuable support for metabolite annotation. We have trained state-of-the-art machine learning regressors using the 80, 038 experimental retention times from the METLIN Small Molecule Retention Tim (SMRT) dataset. The models included deep neural networks, deep kernel learning, several gradient boosting models, and a blending approach. 5, 666 molecular descriptors and 2, 214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software. The models were trained using only the descriptors, only the fingerprints, and both types of features simultaneously. Bayesian hyperparameter search was used for parameter tuning. To avoid data-leakage when reporting the performance metrics, nested cross-validation was employed. The best results were obtained by a heavily regularized deep neural network trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of $$39.2 \pm 1.2\; s$$ 39.2 ± 1.2 s and $$17.2 \pm 0.9\;s$$ 17.2 ± 0.9 s , respectively. To the best of our knowledge, these are the most accurate predictions published up to date over the SMRT dataset. To project retention times between chromatographic methods, a novel Bayesian meta-learning approach that can learn from just a few molecules is proposed. By applying this projection between the deep neural network retention time predictions and a given chromatographic method, our approach can be integrated into a metabolite annotation workflow to obtain z-scores for the candidate annotations. To this end, it is enough that just as few as 10 molecules of a given experiment have been identified (probably by using pure metabolite standards). The use of z-scores permits considering the uncertainty in the projection when ranking candidates, and not only the accuracy. In this scenario, our results show that in 68% of the cases the correct molecule was among the top three candidates filtered by mass and ranked according to z-scores. This shows the usefulness of this information to support metabolite annotation. Python code is available on GitHub at https://github.com/constantino-garcia/cmmrt.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic metabolite annotation using retention time prediction and meta-learned projections

et al. 2022

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“…The addition of MDs based on 3D Molecular Interaction Fields, or Volsurf+, expanded the descriptor space and have been applied to promote the correct molecule for improved identification of MS features [25,26]. A quantum leap beyond those previously published, fingerprints describing 80,038 analytes with documented retention times fed a deep learning model, creating the METLIN SMRT database [27], which has now been extended to nano-LC as well [28].…”

Section: Introductionmentioning

confidence: 99%

Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification

et al. 2021

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A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R2 > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.

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Training Deep Learning Neural Networks for Predicting CCS Using the METLIN-CCS Dataset

Ramajo,

García,

Gil

et al. 2024

Lecture Notes in Computer Science

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Machine learning to predict retention time of small molecules in nano-HPLC

Cited by 32 publications

References 46 publications

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification

Training Deep Learning Neural Networks for Predicting CCS Using the METLIN-CCS Dataset

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