Advancing drug discovery through comprehensive blood-brain barrier permeability predictions
BrainRoute is a comprehensive platform for predicting and analyzing blood-brain barrier (BBB) permeability of small molecules. Our platform integrates experimental data, computational predictions, and advanced filtering criteria to help researchers identify promising drug candidates for neurological applications.
The platform contains a curated dataset of 3,800+ molecules with computed physicochemical and structural properties, filtered through multiple drug candidacy rules to provide reliable predictions for BBB penetration.
BrainRoute predictions and computed properties are intended as research screening aids. They should be reviewed alongside experimental evidence and domain expertise, not treated as definitive ground truth.
Tags describe where each molecule came from and how much review it has received on the platform.
Molecules used to train the BrainRoute prediction models.
Molecules added after a user prediction from the BrainRoute Streamlit prediction tool.
Molecules reviewed through the platform verification workflow using user-submitted experimental data.
The CNS MPO score is a multiparameter optimization score that summarizes how CNS-like a molecule is based on properties linked to brain exposure. Higher scores generally suggest a compound has a more favorable balance of physicochemical properties for central nervous system drug discovery.
In BrainRouteDB, the score is calculated from component scores for lipophilicity, molecular weight, topological polar surface area, hydrogen-bond donors, and ionization-related behavior. These components are combined into a single approximate CNS MPO score for quick comparison across molecules.
The original CNS MPO method uses measured or calculated LogD at physiological pH. BrainRouteDB does not currently calculate true LogD for every molecule in the database, so the platform uses available computed descriptors as a practical approximation. For newly predicted Streamlit molecules, LogD may fall back to LogP when a dedicated LogD calculation is unavailable. Treat this score as a screening aid, not as a replacement for experimental CNS exposure or full pharmacokinetic evaluation.
We aggregate molecular data from existing databases and experimental studies, focusing on compounds with known BBB permeability profiles.
Physicochemical and structural properties are computed for each molecule using the PADEL tool, including molecular weight, logP, polar surface area, and more.
Molecules are evaluated against five industry-standard drug candidacy rules (Lipinski, Veber, Egan, Ghose, and PAINS) to ensure data quality and drug-likeness.
BBB permeability is predicted using machine learning models. Researchers can contribute experimental data to verify predictions and improve model accuracy.
Topological Polar Surface Area measures the sum of surfaces of polar atoms in a molecule. Important for BBB permeability as highly polar molecules struggle to cross the BBB.
Logarithm of the partition coefficient between octanol and water. Determines membrane permeability and is critical for BBB penetration.
Larger molecules generally have reduced BBB permeability.
Classification of molecules with aromatic rings vs. non-aromatic, affecting metabolic stability.
Number of rings and presence of heteroatoms influence drug metabolism and BBB crossing.
Classification of peptide-like and lipid-like molecules which have distinct permeability profiles.
Predicts oral bioavailability by assessing drug-likeness. Compounds violating rules are less likely to be good drugs.
Pharmacokinetic guideline predicting oral bioavailability based on molecular flexibility and polarity.
Predicts blood-brain barrier and human oral bioavailability using lipophilicity and polarity.
Defines "drug-like" chemical space based on molecular properties commonly found in successful drugs.
Pan-Assay Interference compoundS filters identify molecules that frequently produce false positives in biological assays, reducing experimental noise and improving data quality.
Quality Control: These filters remove compounds with unfavorable drug-like properties, ensuring the dataset contains molecules with realistic potential for development.
BBB Relevance: Rules like Egan and Veber directly address BBB permeability, making them ideal for neurological drug discovery.
Industry Standard: These criteria are widely used in pharmaceutical research and have decades of validation behind them.
Comprehensive Coverage: Using multiple rules provides complementary perspectives on drug-likeness, reducing bias from any single filter.
Reproducibility: Transparent, rule-based filtering ensures consistency and allows researchers to understand exactly how the dataset was curated.
Filter molecules by physicochemical, structural, and drug-likeness properties.
View comprehensive molecular information including all calculated properties.
Machine learning-based predictions for blood-brain barrier permeability.
Submit experimental data to improve predictions and validate results.
Download molecular data and predictions for further analysis.
All data freely available to support neurological drug discovery.
BrainRoute is developed by a dedicated team of researchers and developers passionate about advancing CNS drug discovery. Get in touch with us for collaborations, questions, or feedback.
Project Supervision & Co-Lead
Institute for Genomic Medicine Research
📧 laitanawe@gmail.comHave questions or suggestions? Reach out to our team leads:
Interested in collaborating on research or expanding BrainRoute?
→ Propose a collaborationExplore our dataset, submit experimental data, or learn more about how BrainRoute can support your drug discovery research.