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 compound...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.展开更多
基金supported by the National Key Research and Development Program of China(2023YFF0612600 and 2018YFC1602400).
文摘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.
