Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds
Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications. Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Research |
| Online Access: | https://spj.science.org/doi/10.34133/research.0607 |
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| author | Mengdie Fan Chenhui Sang Hua Li Yue Wei Bin Zhang Yang Xing Jing Zhang Jie Yin Wei An Bing Shao |
| author_facet | Mengdie Fan Chenhui Sang Hua Li Yue Wei Bin Zhang Yang Xing Jing Zhang Jie Yin Wei An Bing Shao |
| author_sort | Mengdie Fan |
| collection | DOAJ |
| description | Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications. Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. However, existing retention time prediction methods usually suffer from the scarcity of comprehensive training datasets, sparsity of valid data, and lack of classification in datasets, resulting in poor generalization capability and accuracy. In this study, a dataset for 10,905 compounds was constructed including their retention times. Next, an innovative classification system was implemented, classifying 10,905 compounds into a 3-tier hierarchy across 141 classes, based on functional group weighting. Then, data augmentation was performed within each category using simplified molecular input line entry system (SMILES) enumeration combined with structural similarity expansion. Finally, by training the optimal quantitative structure–retention relationship (QSRR) models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period, a novel and universal high-throughput retention time prediction model was established. The results demonstrate that this model achieves an R2 of 0.98 and an average prediction error of 23 s, outperforming currently published models. This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants, data mining, nontargeted screening, etc. |
| format | Article |
| id | doaj-art-856bb3313a3144cea7b1596d16bda604 |
| institution | Kabale University |
| issn | 2639-5274 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj-art-856bb3313a3144cea7b1596d16bda6042025-02-07T08:00:37ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742025-01-01810.34133/research.0607Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic CompoundsMengdie Fan0Chenhui Sang1Hua Li2Yue Wei3Bin Zhang4Yang Xing5Jing Zhang6Jie Yin7Wei An8Bing Shao9National Key Laboratory of Veterinary Public Health Security, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, and Beijing Laboratory for Food Quality and Safety, Beijing 100193, China.Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China.National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.National Key Laboratory of Veterinary Public Health Security, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, and Beijing Laboratory for Food Quality and Safety, Beijing 100193, China.Accurate prediction of liquid chromatographic retention times is becoming increasingly important in nontargeted screening applications. Traditional retention time approaches heavily rely on the use of standard compounds, which is limited by the speed of synthesis and manufacture of standard products, and is time-consuming and labor-intensive. Recently, machine learning and artificial intelligence algorithms have been applied to retention time prediction, which show unparalleled advantages over traditional experimental methods. However, existing retention time prediction methods usually suffer from the scarcity of comprehensive training datasets, sparsity of valid data, and lack of classification in datasets, resulting in poor generalization capability and accuracy. In this study, a dataset for 10,905 compounds was constructed including their retention times. Next, an innovative classification system was implemented, classifying 10,905 compounds into a 3-tier hierarchy across 141 classes, based on functional group weighting. Then, data augmentation was performed within each category using simplified molecular input line entry system (SMILES) enumeration combined with structural similarity expansion. Finally, by training the optimal quantitative structure–retention relationship (QSRR) models for each category of compounds and selecting the best-fitting model for prediction via discriminant analysis during the prediction period, a novel and universal high-throughput retention time prediction model was established. The results demonstrate that this model achieves an R2 of 0.98 and an average prediction error of 23 s, outperforming currently published models. This study provides a scientific basis for high throughput and rapid prediction of unknown pollutants, data mining, nontargeted screening, etc.https://spj.science.org/doi/10.34133/research.0607 |
| spellingShingle | Mengdie Fan Chenhui Sang Hua Li Yue Wei Bin Zhang Yang Xing Jing Zhang Jie Yin Wei An Bing Shao Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds Research |
| title | Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds |
| title_full | Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds |
| title_fullStr | Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds |
| title_full_unstemmed | Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds |
| title_short | Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds |
| title_sort | development of an efficient and generalized mtscam model to predict liquid chromatography retention times of organic compounds |
| url | https://spj.science.org/doi/10.34133/research.0607 |
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