ArticleInternational journal of molecular sciences2021
Structure Driven Prediction of Chromatographic Retention Times: Applications to Pharmaceutical Analysis.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
- Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.Analytical and bioanalytical chemistry · 2024Article
- An improved workflow for the development of MS-compatible liquid chromatography assay purity and purification methods by using automated LC Screening instrumentation and in silico modeling.Analytical and bioanalytical chemistry · 2024Article
- Chemistry towards Biology.International journal of molecular sciences · 2023Article
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5 authors.
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Abstract
Pharmaceutical drug development relies heavily on the use of Reversed-Phase Liquid Chromatography methods. These methods are used to characterize active pharmaceutical ingredients and drug products by separating the main component from related substances such as process related impurities or main component degradation products. The results presented here indicate that retention models based on Quantitative Structure Retention Relationships can be used for de-risking methods used in pharmaceutical analysis and for the identification of optimal conditions for separation of known sample constituents from postulated/hypothetical components. The prediction of retention times for hypothetical components in established methods is highly valuable as these compounds are not usually readily available for analysis. Here we discuss the development and optimization of retention models, selection of the most relevant structural molecular descriptors, regression model building and validation. We also present a practical example applied to chromatographic method development and discuss the accuracy of these models on selection of optimal separation parameters.
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