BBBP-Atlas is proposed, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP, which offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug discovery.
Abstract
Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system drug discovery, yet existing models are often limited by their reliance on predefined physicochemical descriptors, small-molecule-centered training sets, or conformation-dependent representations, which restricts their transferability across chemically diverse modalities especially peptides. In addition, publicly available BBBP datasets remain fragmented, inconsistently standardized, and weakly controlled for molecular redundancy, increasing the risk of data leakage and overestimated model performance. In this study, we propose BBBP-Atlas, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP. Designed to bypass descriptor and conformation dependencies, our model represents standardized molecular structures as atom-level graphs to capture local atom-bond environments and long-range topological dependencies associated with BBB transport. This design enables direct learning of structure-permeability relationships from molecular topology. For model training and evaluation, we curated a cross-modal, redundancy-filtered database OmniBBBP that seamlessly unifies small molecules and complex peptides, containing 10,218 unique compounds with 9,316 small molecules and 902 peptides. BBBP-Atlas achieved an accuracy of 0.8914 and an MCC of 0.7678 on the independent test set. On a balanced external benchmark of 200 compounds, our model reached an AUC of 0.9108, an accuracy of 0.8500, and an MCC of 0.7000, outperforming LightBBB by an absolute MCC gain of 6%. Case studies further showed that BBBP-Atlas captured clinically meaningful BBB permeability patterns, correctly identifying lorlatinib as BBB-permeable and vancomycin as BBB-impermeable with high confidence. The OmniBBBP-backed BBBP-Atlas offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug discovery. BBBP-Atlas is available at https://cadd.drugflow.com/bbbp/.
Design time prioritization of central nervous system (CNS) drug candidates remains a challenge due to the restrictive nature of the blood–brain barrier (BBB) governing brain exposure. Although various multiparameter optimization (MPO) strategies have guided CNS medicinal chemistry for over a decade, existing frameworks rely heavily on heuristic cutoffs and offer limited interpretability across chemically diverse scaffolds. Here, we introduce a next generation CNS-MPO framework─Aragen-iMPO, (A-iMPO)─which was developed using 5,129 curated compounds through an integrated workflow combining explainable machine learning, rigorous descriptor selection, and low-dimensional discriminant mapping. This yielded six chemically intuitive features capturing polarity, ionization, size, rigidity, and electronic distribution. The resulting score provides a transparent discriminant function enabling direct compound prioritization through a simple threshold rule. Across internal and external validation sets, A-iMPO outperformed established CNS-focused scoring methods while maintaining mechanistic interpretability, providing a practical and design ready tool for CNS drug discovery.
S. Phukan, Ashis Nandy, M. Ramarao· ACS Medicinal Chemistry Lett...· 0 citations
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.
S. Tiwari, Katarzyna Mądra-Gackowska, Marcin Gackowski et al.· Pharmaceutics· 0 citations
Blood-brain barrier permeability (BBBP) prediction is a critical screening task in central nervous system drug discovery, where candidate molecules must be assessed for whether they can cross, or should be prevented from crossing, the blood-brain barrier. However, this task remains challenging because of limited, class-imbalanced datasets and sensitivity to molecular structure. Recent advances in deep learning have established graph neural networks (GNNs) as a powerful approach for molecular representation learning, while pre-trained molecular GNNs provide transferable knowledge for downstream tasks. However, full fine-tuning is often parameter-inefficient and prone to overfitting, whereas existing parameter-efficient fine-tuning (PEFT) methods mainly adapt node features or the two-dimensional covalent graph, limiting their ability to capture three-dimensional geometry and second-order interactions. To address these limitations, we propose BBBP-GeoPEFT, a geometry-informed PEFT framework for pre-trained molecular GNNs. BBBP-GeoPEFT constructs distance-based graphs at multiple cutoffs and their corresponding line graphs from molecular conformers to capture spatial atom and second-order edge interactions. Lightweight auxiliary geometric graph encoders generate cutoff-specific representations, which are incorporated into each pre-trained layer through node-wise cutoff attention and gated residual connections. This design preserves pre-trained knowledge while incorporating permeability-relevant geometric information with a small trainable-parameter budget. Experiments on a curated BBBP dataset show that BBBP-GeoPEFT achieves competitive performance compared with full fine-tuning and representative PEFT baselines. Under both random and scaffold splitting, BBBP-GeoPEFT achieves competitive or improved ROC-AUC and accuracy in most experiments while updating only 10.1% of the model parameters.
Marco Vieto Vega, Long D. Nguyen, Binh P. Nguyen· 0 citations
Blood-brain barrier (BBB) permeability is a critical determinant in the development of central nervous system therapeutics because it directly influences the ability of drug candidates to reach their target sites within the brain. In this study, an explainable machine learning framework was developed to predict BBB permeability using molecular descriptors generated from the MoleculeNet BBBP dataset with the RDKit cheminformatics toolkit. Fifteen physicochemical descriptors extracted from 2,039 compounds were used to train four supervised machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Hyperparameter optimization was performed using GridSearchCV, while model interpretability was investigated using SHapley Additive exPlanations (SHAP). Among the evaluated models, the optimized XGBoost classifier achieved the best predictive performance, with an accuracy of 88.97%, a precision of 88.92%, a recall of 97.76%, an F1-score of 93.13%, and a ROC-AUC of 0.9282. Stratified five-fold cross-validation further demonstrated the robustness of the proposed model, yielding a mean ROC-AUC of 0.8982 +/- 0.0130. Feature importance and SHAP analyses consistently identified TPSA, HBD, and LogP as the most influential molecular descriptors governing BBB permeability prediction. Overall, the proposed framework provides an accurate, interpretable, and computationally efficient approach for BBB permeability prediction and may serve as a valuable tool for the early-stage screening of CNS drug candidates.
OBJECTIVE
The selective permeability of the blood-brain barrier (BBB) hinders the delivery of central nervous system (CNS) drugs to brain targets. It is therefore crucial to determine BBB permeability during CNS drug development.
METHODS
We propose BiGranMolNet (Bi-Granularity Molecular Graph Network), a BBB permeability prediction model based on graph convolutional networks. We integrated datasets from multiple sources to construct comprehensive benchmarks containing 2148 regression samples and 16,904 classification samples. Molecular graphs were constructed at two granularities (atom-level and motif-level), and graph convolutional networks were used to learn representations for each granularity. A cross-attention mechanism fused the dual-granularity features, and a weighted loss function was introduced to mitigate class imbalance.
RESULTS
In 5-fold cross-validation and structure-aware evaluations, BiGranMolNet achieved stable and competitive performance on both regression and classification tasks.
CONCLUSIONS
By integrating molecular structural information at atomic and motif levels, BiGranMolNet provides a reliable computational tool for BBB permeability prediction. The proposed framework can support early-stage CNS drug screening by prioritizing compounds with favorable brain exposure potential and by offering structure-aware clues for molecular optimization.
Yi-Dan Wang, Yi-Zhuo Wang, Yang Wang et al.· Journal of Biomedical Inform...· 0 citations
The blood-brain barrier (BBB) regulates the entry of chemicals into the central nervous system, and contaminants with high permeability can accumulate in the brain, posing neurotoxicity risks. Given the urgent need for high-throughput neurotoxicity assessment, this study proposes a multifeature fusion framework for identifying and quantifying the BBB permeability to neurotoxic chemicals. BBBProfiler leverages an SE(3)-Transformer to encode the three-dimensional molecular geometry and a deep neural network to encode collision cross-section (CCS) and fingerprint, followed by adaptive integration of modality representations through gating weights. Under the scaffold-split evaluation, BBBProfiler achieves high performance with an area under the receiver operating characteristic curve (AUC) of 93.72% and a recall of 89.19% for classification, and a coefficient of determination (R2) of 0.72 for regression. Its predictive performance was validated using a BBB bioassay (11 chemicals) and an external data set (2023 chemicals), in which the model achieved 94.42% AUC. Substructure mask explanation analysis revealed CCS as an influential feature that correlated well with BBB permeability to chemicals (R2 = 0.84). Model predictions were supplemented with natural-language explanations generated from a large language model. BBBProfiler is deployed on a publicly accessible platform (https://www.ai4environ.cn/BBBProfiler) to facilitate the development of new approach methodologies for neurotoxicity evaluation.
Lilai Shen, Zi-Yu Chen, Weiwei Huan et al.· Environmental Science and Te...· 0 citations
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MIT News · Artificial Intelligence· news.mit.eduAug 18, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Microsoft Research Blog· microsoft.comJul 30, 2026
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MIT News · Artificial Intelligence· news.mit.eduMay 20, 2026
