LEMTL: Enhancing the pharmacokinetic predictions of multitask learning with existing pharmacokinetics

Evaluation of pharmacokinetic properties such as absorption, distribution, metabolism, excretion, and toxicity is essential but costly and time-consuming in the research and development of new drug candidates. This challenge has driven many researchers to use multitask learning for in silico predictions. Given extra information such as SMILES (simplified molecular input line entry system) as input data, existing multitask learning methods can usually have a good performance in predicting pharmacokinetics, which are served as labels/tasks. However, most of these methods are based solely on additional information, overlooking potentially available pharmacokinetics, which can be utilized to further enhance the prediction of unavailable ones. To address this limitation, we propose Label Enhanced MultiTask Learning (LEMTL), a framework to improve the prediction accuracy of unknown pharmacokinetics of existing multitask learning methods by aggregating shared feature representations learned from additional information and from known pharmacokinetics. Experiments on synthetic and real datasets demonstrate significant improvements over the associated multitask learning approaches when incorporated within the LEMTL framework.