A molecule's pharmacokinetic properties play an essential role in designing and developing new therapeutics . However, experimental pharmacokinetic data from in vitro and vivo experiments is scarce, as it is expensive to generate, making its approximation a priority for machine learning research . To evaluate our models' performance on pharmacokinetic tasks, we use the data set recently published by Fang et al. [31]. The data set contains 3,521 commercially available compounds that were tested against the following endpoints in Biogen ADME in vitro assays: Human liver microsomal stability (HLM, clearance in mL/min/kg), MDR1-MDCK efflux ratio (MDR1-MDCK ER, permeability with Madin Darby Canine Kidney cells transfected with MDR1), solubility at a pH of 6.8 (Solubility, ug/mL), rat liver microsomal stability (RLM, clearance in mL/min/kg), human plasma protein binding (hPPB, percent unbound), and rat plasma protein binding (rPPB, percent unbound). Fang et al. [31] provide baselines trained on the data set for random forests (RF), gradient boosting (LightGBM), a hyperparameter-tuned directed message-passing neural network (D-MPNN), and a hyperparameter-tuned directed message-passing neural network plus precomputed RDKit 2D descriptors (D-MPNN+) . Values for additional models can be found in the original publication. As input for RF and LightGBM, Fang et al. [31] concatenated 1024-bit binary functional connectivity fingerprint with radius 4 (FCFP4) concatenated with 316 2D descriptors that were precomputed using the RDKit package . In addition, we finetuned the publicly available pretrained variant of MolFormer on the Biogen ADME data set (see Section 4.6).