A major shortcoming for typical QSRR modeling approaches is that fitted models can only serve predictively under the LC method conditions used to collect data. More recently, the literature has seen artificial neural networks successfully utilized for predicting retention times of small molecules as an alternative to traditional QSRR modeling approaches such as random forest and support vector regression . To leverage disparate datasets, various approaches using artificial neural networks have been reported . Utilizing a technique called transfer learning , artificial neural networks have shown greater performance on retention time modeling for smaller datasets when pre-trained on larger datasets of differing LC methodology. Additionally, the field of retention time modeling has seen much success when using architectures that process the chemical structures of analytes as graphs .