Evaluating the Incremental Value of Three-Dimensional Conformer Descriptors for Blood-Brain Barrier Permeability...

Background/Objectives: Predicting blood-brain barrier (BBB) permeability remains a major challenge in central nervous system (CNS) drug discovery. Three-dimensional (3D) conformer-derived molecular descriptors are often proposed as improvements over conventional two-dimensional (2D) topological representations; however, their incremental predictive value remains unclear. Methods: Here, we systematically evaluated six molecular feature sets comprising curated 2D Mordred descriptors, Morgan/ECFP4 fingerprints, 3D conformer-derived descriptors, and their combinations, using LightGBM regression on the B3DB benchmark dataset (1054 compounds with experimental logBB values). The model performance was assessed using 30 independent random splits and 30 Murcko scaffold-based splits. Results: The 2D descriptor model achieved mean test R² values of 0.567 ± 0.060 and 0.418 ± 0.085 under random and scaffold splitting, respectively, whereas the 3D descriptor model performed substantially worse (0.430 ± 0.066 and 0.298 ± 0.097, respectively). Incorporating 3D descriptors into the 2D feature set yielded only a marginal improvement under random splitting (+0.008 R²; p = 0.039), which disappeared during scaffold-based validation (p = 0.599). Similarly, adding 3D descriptors to the combined 2D + fingerprint representation did not yield any significant benefits. Fingerprints alone exhibited pronounced scaffold fragility, with the mean R² decreasing from 0.441 to 0.312 between the random and scaffold evaluations. SHAP analysis identified TopoPSA (NO) as the dominant predictor (mean |SHAP| = 0.332), highlighting the central role of desolvation in BBB permeation. Applicability domain analysis showed that 94.8% of the test compounds fell within the structural coverage of the training set. Conclusions: Overall, on the B3DB benchmark and under the descriptor implementation evaluated here, 3D conformer descriptors provided limited incremental value and no scaffold-robust advantage over well-curated 2D molecular representations for BBB permeability predictions.