bioRxiv benchmark finds no dominant model family in ADMET prediction

[bioRxiv] A bioRxiv preprint by Stamenov et al. benchmarks graph neural networks, frozen pretrained models (ChemBERTa, MolCLR) and fingerprint baselines across six Therapeutics Data Commons ADMET endpoints under scaffold-split evaluation, giving every model family a matched 50-trial, validation-selected tuning budget and reporting the median over ten seeds. According to the authors, under this fair comparison protocol no model family dominates, and differences between families fall within the seed-to-seed range on most endpoints. The GNN is clearly best only on Caco-2 permeability (R² about 0.48), and a controlled ablation shows this advantage comes entirely from auxiliary physicochemical descriptors rather than the graph representation; Morgan fingerprints are competitive on hERG (AUC about 0.77) and give the best median on both clearance endpoints, while MolCLR is best on Tox21. The authors also report that for hepatocyte clearance and half-life all models yield near-zero or negative R², that none of seven hyperparameter-optimization strategies consistently outperforms random search at this budget, and that selecting the optimizer using test-set performance or reporting only single best-trial runs can substantially inflate a favored model's apparent advantage.