首页|National Institute of the Republic of Serbia Reports Findings in Science (A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention ...)
National Institute of the Republic of Serbia Reports Findings in Science (A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Science is the subject of a report. According to news originating from Belgrade, Serbia, by NewsRx correspondents, research stated, “In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules.” Our news journalists obtained a quote from the research from the National Institute of the Republic of Serbia, “In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mech- anism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), ran- dom forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (r >0.73, order of elution >60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties.”
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