Performance and robustness of small molecule retention time prediction with molecular graph neural networks in industrial drug discovery campaigns
© 2024. The Author(s)..
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-learning framework with the robustness required to support a chemical synthesis production platform. We used internally generated data from high-throughput parallel synthesis in context of pharmaceutical drug discovery projects. We tested machine-learning models from the following frameworks: XGBoost, ChemProp, and DeepChem, using a dataset of 7552 small molecules. Our findings show that two specific models, AttentiveFP and ChemProp, performed better than XGBoost and a regular neural network in predicting RT accurately. We also assessed how well these models performed over time and found that molecular graph neural networks consistently gave accurate predictions for new chemical series. In addition, when we applied ChemProp on the publicly available METLIN SMRT dataset, it performed impressively with an average error of 38.70 s. These results highlight the efficacy of molecular graph neural networks, especially ChemProp, in diverse RT prediction scenarios, thereby enhancing the efficiency of chromatographic analysis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Scientific reports - 14(2024), 1 vom: 16. Apr., Seite 8733 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Vik, Daniel [VerfasserIn] |
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| Links: |
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| Themen: |
Applied artificial intelligence |
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| Anmerkungen: |
Date Completed 18.04.2024 Date Revised 25.04.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-024-59620-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM371166381 |
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| 520 | |a © 2024. The Author(s). | ||
| 520 | |a 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-learning framework with the robustness required to support a chemical synthesis production platform. We used internally generated data from high-throughput parallel synthesis in context of pharmaceutical drug discovery projects. We tested machine-learning models from the following frameworks: XGBoost, ChemProp, and DeepChem, using a dataset of 7552 small molecules. Our findings show that two specific models, AttentiveFP and ChemProp, performed better than XGBoost and a regular neural network in predicting RT accurately. We also assessed how well these models performed over time and found that molecular graph neural networks consistently gave accurate predictions for new chemical series. In addition, when we applied ChemProp on the publicly available METLIN SMRT dataset, it performed impressively with an average error of 38.70 s. These results highlight the efficacy of molecular graph neural networks, especially ChemProp, in diverse RT prediction scenarios, thereby enhancing the efficiency of chromatographic analysis | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Applied artificial intelligence | |
| 650 | 4 | |a Chromatography | |
| 650 | 4 | |a Machine-learning | |
| 650 | 4 | |a Pharmaceuticals | |
| 650 | 4 | |a Retention time | |
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| 700 | 1 | |a Mudaliar, Chirag |e verfasserin |4 aut | |
| 700 | 1 | |a Nørregaard-Madsen, Mads |e verfasserin |4 aut | |
| 700 | 1 | |a Kontijevskis, Aleksejs |e verfasserin |4 aut | |
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