Figure 7

Download original image
AI-driven prediction of compound separation. (A) High-throughput TLC robotic platform: This sophisticated platform is designed for large-scale standardization and measurement of Rf values, subsequently generating substantial volumes of normalized TLC data. These data are then utilized for regression analysis through machine learning techniques [52]. Copyright©2022, Elsevier. (B) Predicting chromatographic enantiomer separation: By leveraging a literature-based dataset (CMRT dataset) of retention times for chiral molecules in HPLC, establishing a correlation between molecular structure and retention time. This is further enhanced by employing the quantile geometry augmented graph neural network (QGeoGNN) for achieving optimal prediction accuracy [67]. Copyright©2022, Springer Nature.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.