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Singh YR, Shah DB, Kulkarni M, Patel SR, Maheshwari DG, Shah JS, Shah S. Current trends in chromatographic prediction using artificial intelligence and machine learning. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:2785-2797. [PMID: 37264667 DOI: 10.1039/d3ay00362k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) gained tremendous growth and are rapidly becoming popular in various fields of prediction due to their potential abilities, accuracy, and speed. Machine learning algorithms employ historical data to analyze or predict information using patterns or trends. AI and ML were most employed in chromatographic predictions and particularly attractive options for liquid chromatography method development, as they can help achieve desired results faster, more accurately, and more efficiently. This review aims at exploring various AI and ML models employed in the determination of chromatographic characteristics. This review also aims to provide deep insight into reported artificial neural network (ANN) associated techniques which maintained better accuracy and significant possibilities for chromatographic characteristics prediction in liquid chromatography over classical linear models and also emphasizes the integration of a fuzzy system with an ANN, as this integrated study provides more efficient and accurate methods in chromatographic prediction than other linear models. This study also focuses on the retention prediction of a target molecule employing QSRR methodology combined with an ANN, highlighting a more effective technique than the QSRR alone. This approach showed the benefits of combining AI or ML algorithms with the QSRR to obtain more accurate retention predictions, emphasizing the potential of artificial intelligence and machine learning for overcoming adversities in analytical chemistry.
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Affiliation(s)
- Yash Raj Singh
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Darshil B Shah
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Mangesh Kulkarni
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Shreyanshu R Patel
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Dilip G Maheshwari
- Department of Pharmaceutical Quality Assurance, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Jignesh S Shah
- Department of Pharmaceutical Regulatory Affairs, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
| | - Shreeraj Shah
- Department of Pharmaceutical Technology, LJ Institute of Pharmacy, LJ University, Ahmedabad, Gujarat, India
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Kumari P, Van Laethem T, Hubert P, Fillet M, Sacré PY, Hubert C. Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy. Molecules 2023; 28:molecules28041696. [PMID: 36838689 PMCID: PMC9964055 DOI: 10.3390/molecules28041696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/05/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.
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Affiliation(s)
- Priyanka Kumari
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Correspondence: (P.K.); (C.H.); Tel.: +32-(0)-43664326 (C.H.)
| | - Thomas Van Laethem
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Philippe Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Pierre-Yves Sacré
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
| | - Cédric Hubert
- Department of Pharmacy, Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiege), CIRM, Quartier Hopital (B36 Tower 4), Avenue Hippocrate, 4000 Liège, Belgium
- Correspondence: (P.K.); (C.H.); Tel.: +32-(0)-43664326 (C.H.)
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CORAL: Quantitative Structure Retention Relationship (QSRR) of flavors and fragrances compounds studied on the stationary phase methyl silicone OV-101 column in gas chromatography using correlation intensity index and consensus modelling. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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A Brief Review of Chromatography in Croatia. SEPARATIONS 2022. [DOI: 10.3390/separations9060134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Although the Republic of Croatia is a relatively small country geographically, it can boast numerous scientists who have left indelible marks in various scientific fields. However, this paper is exclusively about chromatography as one of the most important analytical techniques of our time. The development of chromatography in the Republic of Croatia and the role that three institutions have played in it—the Faculty of Chemical Engineering and Technology of the University of Zagreb, the Croatian Society of Chemical Engineers, and the Central European Group for Separation Sciences—will be briefly discussed.
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Kensert A, Bouwmeester R, Efthymiadis K, Van Broeck P, Desmet G, Cabooter D. Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data. Anal Chem 2021; 93:15633-15641. [PMID: 34780168 DOI: 10.1021/acs.analchem.1c02988] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Machine learning is a popular technique to predict the retention times of molecules based on descriptors. Descriptors and associated labels (e.g., retention times) of a set of molecules can be used to train a machine learning algorithm. However, descriptors are fixed molecular features which are not necessarily optimized for the given machine learning problem (e.g., to predict retention times). Recent advances in molecular machine learning make use of so-called graph convolutional networks (GCNs) to learn molecular representations from atoms and their bonds to adjacent atoms to optimize the molecular representation for the given problem. In this study, two GCNs were implemented to predict the retention times of molecules for three different chromatographic data sets and compared to seven benchmarks (including two state-of-the art machine learning models). Additionally, saliency maps were computed from trained GCNs to better interpret the importance of certain molecular sub-structures in the data sets. Based on the overall observations of this study, the GCNs performed better than all benchmarks, either significantly outperforming them (5-25% lower mean absolute error) or performing similar to them (<5% difference). Saliency maps revealed a significant difference in molecular sub-structures that are important for predictions of different chromatographic data sets (reversed-phase liquid chromatography vs hydrophilic interaction liquid chromatography).
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Affiliation(s)
- Alexander Kensert
- Department for Pharmaceutical and Pharmacological Sciences, University of Leuven (KU Leuven), Pharmaceutical Analysis, Herestraat 49, Leuven 3000, Belgium.,Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussel 1050, Belgium
| | - Robbin Bouwmeester
- VIB, VIB-UGent Center for Medical Biotechnology, Technologiepark-Zwijnaarde 75, Gent 9052, Belgium.,Department of Biomolecular Medicine, Ghent University, Technologiepark-Zwijnaarde 75, Gent 9052, Belgium
| | - Kyriakos Efthymiadis
- Department for Pharmaceutical and Pharmacological Sciences, University of Leuven (KU Leuven), Pharmaceutical Analysis, Herestraat 49, Leuven 3000, Belgium.,Department of Computer Science, Artificial Intelligence Lab, Vrije Universiteit Brussel, Pleinlaan 9, Brussel 1050, Belgium
| | - Peter Van Broeck
- Department of Pharmaceutical Development and Manufacturing Sciences, Janssen Pharmaceutica, Turnhoutseweg 30, Beerse 2340, Belgium
| | - Gert Desmet
- Department of Chemical Engineering, Vrije Universiteit Brussel, Pleinlaan 2, Brussel 1050, Belgium
| | - Deirdre Cabooter
- Department for Pharmaceutical and Pharmacological Sciences, University of Leuven (KU Leuven), Pharmaceutical Analysis, Herestraat 49, Leuven 3000, Belgium
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Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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Safdel F, Safa F. Atom-Type-Based AI Topological Indices for Artificial Neural Network Modeling of Retention Indices of Monomethylalkanes. J Chromatogr Sci 2019; 57:1-8. [PMID: 30169788 DOI: 10.1093/chromsci/bmy081] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Indexed: 11/14/2022]
Abstract
In this work, a combination of Xu and atom-type-based AI topological indices (TIs) were employed for quantitative structure-retention relationship (QSRR) study of monomethylalkanes (MMAs). A total of 196 temperature-programmed gas chromatographic retention indices corresponding to all C4-C30 MMAs on OV-1 stationary phase have been used in QSRR modeling. Results of the study showed that an artificial neural network (ANN) with 4-9-1 topology and Levenberg-Marquardt training algorithm can predict the retention indices with high degree of accuracy. The statistics of root-mean-square error for the training, validation and test sets were 0.200, 0.316 and 0.215, respectively. The proposed model resulted in a maximum relative error of 0.24% suggesting the TIs as excellent alternative for estimating retention indices of MMAs. According to the obtained results, relative importance of the TIs decreased in the order of AI(-CH3)> AI(-CH2-)> AI(>CH-)> Xu showing significant role of molecular branching, steric factor and molecular size as effective structural features on retention indices of MMAs.
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Affiliation(s)
- Farah Safdel
- Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, I.R. Iran
| | - Fariba Safa
- Department of Chemistry, Rasht Branch, Islamic Azad University, Rasht, I.R. Iran
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Amos RI, Haddad PR, Szucs R, Dolan JW, Pohl CA. Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography. Trends Analyt Chem 2018. [DOI: 10.1016/j.trac.2018.05.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography. J Chromatogr A 2017; 1523:173-182. [DOI: 10.1016/j.chroma.2017.02.054] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Revised: 02/20/2017] [Accepted: 02/23/2017] [Indexed: 11/19/2022]
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Park SH, Haddad PR, Amos RI, Talebi M, Szucs R, Pohl CA, Dolan JW. Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. III Combination of Tanimoto similarity index, log P , and retention factor ratio to identify optimal analyte training sets for ion chromatography. J Chromatogr A 2017; 1520:107-116. [DOI: 10.1016/j.chroma.2017.09.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 09/02/2017] [Accepted: 09/06/2017] [Indexed: 11/17/2022]
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Park SH, Haddad PR, Talebi M, Tyteca E, Amos RI, Szucs R, Dolan JW, Pohl CA. Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model. J Chromatogr A 2017; 1486:68-75. [DOI: 10.1016/j.chroma.2016.12.048] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 12/14/2016] [Accepted: 12/16/2016] [Indexed: 10/20/2022]
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Žuvela P, Jay Liu J. On feature selection for supervised learning problems involving high-dimensional analytical information. RSC Adv 2016. [DOI: 10.1039/c6ra09336a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Feature selection for supervised learning problems involving analytical information.
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Affiliation(s)
- P. Žuvela
- Department of Chemical Engineering
- Pukyong National University
- Busan
- Korea
| | - J. Jay Liu
- Department of Chemical Engineering
- Pukyong National University
- Busan
- Korea
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Bade R, Bijlsma L, Miller TH, Barron LP, Sancho JV, Hernández F. Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2015; 538:934-41. [PMID: 26363605 DOI: 10.1016/j.scitotenv.2015.08.078] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2015] [Revised: 08/14/2015] [Accepted: 08/14/2015] [Indexed: 04/14/2023]
Abstract
The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (tR) and mass spectral comparisons. Chromatographic tR prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for tR prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound tR dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85% of all compounds to within 2min of their measured tR for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95% prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.
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Affiliation(s)
- Richard Bade
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain
| | - Lubertus Bijlsma
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain
| | - Thomas H Miller
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Leon P Barron
- Analytical & Environmental Sciences Division, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, United Kingdom
| | - Juan Vicente Sancho
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain
| | - Felix Hernández
- Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat, E-12071 Castellón, Spain.
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Žuvela P, Liu JJ, Macur K, Bączek T. Molecular Descriptor Subset Selection in Theoretical Peptide Quantitative Structure–Retention Relationship Model Development Using Nature-Inspired Optimization Algorithms. Anal Chem 2015; 87:9876-83. [DOI: 10.1021/acs.analchem.5b02349] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Petar Žuvela
- Department
of Chemical Engineering, Pukyong National University, 365 Sinseon-ro, 608-739 Busan, Korea
| | - J. Jay Liu
- Department
of Chemical Engineering, Pukyong National University, 365 Sinseon-ro, 608-739 Busan, Korea
| | - Katarzyna Macur
- Laboratory
of Mass Spectrometry, Intercollegiate Faculty of Biotechnology, University of Gdańsk and Medical University of Gdańsk, Kładki
24, 80-822 Gdańsk, Poland
| | - Tomasz Bączek
- Department
of Pharmaceutical Chemistry, Medical University of Gdańsk, Hallera
107, 80-416 Gdańsk, Poland
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Ukić Š, Novak M, Krilić A, Avdalović N, Liu Y, Buszewski B, Bolanča T. Development of Gradient Retention Model in Ion Chromatography. Part III: Fuzzy Logic QSRR Approach. Chromatographia 2015. [DOI: 10.1007/s10337-015-2845-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Development of Gradient Retention Model in Ion Chromatography. Part II: Artificial Intelligence QSRR Approach. Chromatographia 2014. [DOI: 10.1007/s10337-014-2654-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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