Design novel small molecules optimized for blood-brain barrier penetration from day one. Our generative AI platform creates therapeutic candidates with optimal CNS bioavailability and maintained efficacy.
BORAZON's generative AI platform addresses the most critical bottleneck in neurotherapeutics: blood-brain barrier penetration. Our technology ensures every molecule is optimized for CNS delivery while maintaining potent therapeutic activity.
Our deep learning models generate novel molecular structures with BBB penetration built into the core architecture. Unlike traditional screening approaches that test existing compounds, we create entirely new chemical entities designed specifically for optimal brain delivery. The AI considers over 200 molecular descriptors simultaneously, including TPSA, hydrogen bond donors/acceptors, molecular weight, and rotatable bonds to ensure compliance with CNS drug-like properties.
Every generated molecule maintains the critical lipophilicity window (LogP 1.5-3.0) that maximizes passive diffusion across the BBB while avoiding excessive protein binding and off-target effects. Our models predict octanol-water partition coefficients with R² > 0.91 correlation to experimental values, ensuring molecules have sufficient lipid solubility for membrane permeation without compromising aqueous solubility needed for formulation and systemic distribution.
P-glycoprotein (P-gp) efflux is the primary mechanism limiting CNS drug bioavailability. BORAZON's AI specifically designs molecules to evade P-gp recognition through strategic placement of functional groups and molecular topology optimization. We employ advanced QSAR models trained on over 15,000 P-gp substrates and non-substrates, achieving 89% prediction accuracy. Generated molecules incorporate structural features that minimize efflux ratio while maintaining target engagement.
BBB optimization never comes at the expense of pharmacological activity. Our multi-objective optimization framework simultaneously maximizes target binding affinity, selectivity, and BBB penetration. The platform employs protein-ligand interaction modeling, molecular dynamics simulations, and binding free energy calculations to ensure generated molecules maintain potent engagement with therapeutic targets (typical IC50 < 100nM) while achieving superior CNS bioavailability compared to existing therapeutics.
From initial concept to synthesis-ready candidates, BORAZON provides end-to-end molecular design with complete ADMET profiling and synthetic accessibility analysis.
Generate completely novel molecular structures using transformer-based generative models trained on 2.3 billion drug-like molecules. Our architecture combines variational autoencoders (VAE) with reinforcement learning to explore chemical space while maintaining synthesizability. The system generates molecules atom-by-atom with validity checking at each step, ensuring 99.7% of generated structures are chemically valid. Fine-tuned on CNS-penetrant drugs, the model inherently understands structural features that promote BBB permeability.
Simultaneously optimize across 50+ molecular properties including physicochemical descriptors, ADMET parameters, target affinity, and BBB penetration. Our Pareto-based optimization ensures no single property is maximized at the expense of others. The system balances molecular weight (180-450 Da for optimal BBB), TPSA (<90 Ų), lipophilicity (LogP 1.5-3.0), hydrogen bond donors (<3), rotatable bonds (<10), and target binding affinity. Advanced Bayesian optimization guides the search through multi-dimensional chemical space efficiently.
Predict blood-brain barrier penetration using ensemble models trained on 7,800+ compounds with experimental BBB data. Our hybrid approach combines graph neural networks (GNN) for molecular structure encoding with gradient boosting for property prediction. Models consider passive diffusion mechanisms, active transport, and efflux pump interactions. Predictions include BBB permeability coefficients (log BB), brain-to-plasma ratios, and categorization (CNS+, CNS+/-, CNS-). Cross-validation demonstrates 94% accuracy with AUC 0.96 for binary classification.
Identify and minimize P-gp mediated efflux using deep learning classifiers trained on 15,200+ P-gp substrates and non-substrates. P-gp is the most significant active efflux transporter limiting CNS drug bioavailability. Our models analyze molecular features associated with P-gp recognition including molecular flexibility, hydrogen bonding patterns, and aromatic ring systems. Predictions include substrate probability, efflux ratio estimates, and structural alerts. The platform suggests specific modifications to reduce P-gp liability while maintaining pharmacological activity.
Predict complete ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles for every generated molecule. Models cover intestinal permeability (Caco-2, MDCK), plasma protein binding, volume of distribution, CYP450 metabolism (substrate/inhibitor for 2D6, 3A4, 2C9, 2C19, 1A2), clearance, half-life, hERG cardiotoxicity, hepatotoxicity, mutagenicity (Ames), and phospholipidosis. Each prediction includes confidence intervals. ADMET filters ensure drug-likeness and safety while maintaining BBB penetration focus.
Estimate binding affinity to therapeutic targets using structure-based and ligand-based methods. Protein-ligand interaction models employ graph convolutional networks to encode 3D binding site geometry and predict binding free energies. For targets with known structures, molecular docking with MM-GBSA scoring provides detailed interaction analysis. Ligand-based models use molecular fingerprints and target-specific QSAR to predict IC50/Ki values. Selectivity predictions across protein families help identify potential off-target interactions early in design.
Generate feasible synthesis pathways for every designed molecule using AI-powered retrosynthesis. Our template-free approach employs neural networks trained on 12.4 million chemical reactions from the USPTO and Reaxys databases. The system recursively decomposes target molecules into commercially available starting materials, suggesting 5-15 diverse synthetic routes ranked by feasibility, cost, and step count. Each route includes reagents, conditions, expected yields, and potential side reactions. Synthetic accessibility scores (SA score 1-10) guide molecule generation toward easily synthesizable structures.
Transform existing molecular scaffolds into novel chemotypes with maintained or improved properties. Scaffold hopping identifies alternative core structures that preserve pharmacophore geometry and target binding while potentially improving BBB penetration, metabolic stability, or intellectual property positioning. Bioisosteric replacement systematically substitutes functional groups with isosteric alternatives to optimize properties. The platform suggests classical bioisosteres (e.g., carboxylic acid → tetrazole) and non-classical replacements discovered through AI analysis of successful drug modifications.
From target specification to synthesis-ready candidates in days, not years. Our streamlined workflow accelerates CNS drug discovery.
Specify your therapeutic target, desired pharmacological profile, and design constraints. Input target protein structure (PDB file) or sequence, reference ligands with known activity, and CNS indication. Define property constraints including BBB permeability thresholds (log BB > -1.0), lipophilicity range (LogP 1.5-3.0), molecular weight limits (180-450 Da), and TPSA targets (<90 Ų). Specify required substructures, forbidden moieties (PAINS, reactive groups), and selectivity requirements against off-targets. Our platform also accepts existing lead compounds for optimization.
BORAZON's generative AI creates 10,000-100,000 novel molecular candidates optimized for your specifications. The transformer-based architecture generates molecules token-by-token (SMILES representation), with real-time validation ensuring chemical validity. Reinforcement learning guides generation toward high-scoring molecules across all objectives: BBB permeability, target affinity, ADMET properties, and synthetic accessibility. Multi-objective optimization produces a diverse Pareto frontier of solutions. Generation completes in 24-48 hours, with continuous streaming of top candidates. Each molecule undergoes immediate filtering for drug-likeness, PAINS, and structural alerts.
Download comprehensive reports for top-ranked candidates (typically 100-500 molecules). Each molecule includes complete ADMET predictions with confidence intervals, BBB permeability metrics (log BB, brain-to-plasma ratio), P-gp substrate assessment, target binding predictions, and off-target selectivity analysis. Detailed retrosynthetic routes provide 5-15 feasible synthesis pathways with starting materials, reagents, conditions, and predicted yields. Interactive visualizations show 2D structures, 3D conformations, and protein-ligand binding poses. Export data in SDF, SMILES, and Excel formats. Molecules are prioritized by multi-objective scoring balancing all design criteria.
BORAZON combines state-of-the-art deep learning architectures with extensive chemical and biological databases to create the most advanced BBB-penetrant molecule design platform.
At the heart of BORAZON lies a sophisticated transformer architecture specifically designed for molecular generation. Unlike traditional generative models that treat molecules as fixed structures, our architecture generates molecules sequentially, considering chemical validity and property optimization at each step. The model was pretrained on 2.3 billion drug-like molecules from ZINC, ChEMBL, PubChem, and proprietary sources, learning fundamental chemical grammar, reaction patterns, and structure-property relationships.
Fine-tuning employs reinforcement learning with a sophisticated reward function that simultaneously evaluates BBB permeability, target binding affinity, ADMET properties, synthetic accessibility, and drug-likeness. The reward function uses weighted aggregation with Pareto optimization to maintain diversity and prevent over-optimization of single properties. This approach ensures generated molecules represent true multi-objective solutions rather than compromised designs.
Message passing neural networks (MPNN) encode molecular graphs for property prediction. Atoms and bonds are represented as nodes and edges, with learned embeddings capturing local chemical environments. Graph convolutions aggregate information from neighboring atoms, enabling the model to understand long-range interactions and emergent molecular properties. GNNs achieve superior performance for BBB prediction (94% accuracy) compared to traditional fingerprint-based methods (78-82%).
Shared neural network architecture simultaneously predicts 50+ molecular properties through multi-task learning. This approach improves prediction accuracy through transfer learning—knowledge from data-rich tasks (e.g., lipophilicity with 50,000+ experimental values) transfers to data-sparse tasks (e.g., BBB permeability with 7,800 values). Multi-task models demonstrate 12-18% improved accuracy compared to single-task models, particularly for properties with limited training data.
Efficient exploration of chemical space using Bayesian optimization with Gaussian process surrogate models. After each generation batch, the optimization algorithm updates its understanding of structure-property relationships and suggests the most promising regions of chemical space to explore next. This active learning approach reduces the number of molecules that need evaluation by 10-20x compared to random exploration, dramatically accelerating discovery timelines.
3D protein-ligand interaction modeling using graph convolutional networks trained on crystallographic binding data from PDBbind (19,000+ complexes). Models encode both protein binding site geometry and ligand structure to predict binding affinity and key interaction patterns. For targets without crystal structures, homology modeling combined with molecular dynamics provides structural ensembles for docking. Binding free energy calculations (MM-GBSA, FEP) validate top candidates.
Models pretrained on massive chemical datasets (2.3B molecules) are fine-tuned on CNS-specific data. This transfer learning approach allows the model to leverage general chemical knowledge while specializing in BBB-penetrant molecule design. Pretraining captures fundamental concepts like aromaticity, stereochemistry, functional group reactivity, and common structural motifs. Fine-tuning on 89,000 CNS-active drugs teaches BBB-specific patterns without forgetting general chemistry.
All predictions include uncertainty estimates through ensemble models and Monte Carlo dropout. Understanding prediction confidence is critical for prioritizing experimental validation. High-confidence predictions (narrow confidence intervals) indicate reliable property estimates, while low-confidence predictions suggest areas where experimental validation is particularly valuable. Uncertainty-aware active learning prioritizes acquiring training data for property regimes where models are least confident.
BORAZON accelerates therapeutic development for neurological and psychiatric disorders where BBB penetration is the critical challenge.
Design molecules targeting key pathways in Alzheimer's disease, Parkinson's disease, ALS, and Huntington's disease. BORAZON generates candidates that cross the BBB to modulate protein aggregation (amyloid-beta, tau, alpha-synuclein), neuroinflammation (microglia activation), oxidative stress, and mitochondrial dysfunction. The platform optimizes molecules for brain region-specific delivery while minimizing peripheral side effects.
Develop next-generation therapeutics for depression, anxiety, schizophrenia, and bipolar disorder. BORAZON designs molecules targeting monoamine systems (serotonin, dopamine, norepinephrine receptors), glutamatergic signaling (NMDA, AMPA receptors), and GABAergic neurotransmission. The platform optimizes for rapid brain penetration (important for acute symptoms) while ensuring selectivity to minimize side effects common with psychiatric medications.
Create therapeutics for glioblastoma, brain metastases, and other CNS malignancies where the BBB severely limits drug delivery. BORAZON generates cytotoxic agents, kinase inhibitors, and immunomodulators optimized for brain penetration. The platform balances lipophilicity for BBB crossing with sufficient polarity for tumor cell uptake. Molecules are designed to evade P-gp and BCRP efflux that protect both healthy brain and tumor cells.
Accelerate therapeutic development for rare CNS conditions including lysosomal storage diseases (Krabbe, metachromatic leukodystrophy), epilepsies (Dravet syndrome, Lennox-Gastaut), and inherited metabolic disorders. Many enzyme replacement and gene therapies fail due to BBB impermeability. BORAZON designs small molecules that cross the BBB to address enzyme deficiencies, ion channel dysfunction, or metabolic imbalances.
Design analgesics for neuropathic pain and chronic pain conditions requiring CNS activity. BORAZON generates molecules targeting opioid receptors, sodium channels, NMDA receptors, and inflammatory mediators with optimized BBB penetration. The platform designs molecules with reduced abuse potential compared to traditional opioids through balanced receptor profiles and peripheral-to-central selectivity tuning.
Develop anti-infectives for CNS infections including bacterial meningitis, viral encephalitis, and parasitic diseases where the BBB prevents adequate drug concentrations. BORAZON designs antibiotics, antivirals, and antiparasitics with BBB penetration while maintaining antimicrobial potency. The platform optimizes for rapid brain penetration (critical for acute infections) and considers CSF distribution as well as brain parenchyma penetration.
BORAZON's platform is applicable to any CNS indication where BBB penetration is critical. Contact us to discuss how we can accelerate your specific drug discovery program.
Discuss Your ProjectEvery model, prediction, and design principle in BORAZON is grounded in peer-reviewed science and validated against experimental data.
BORAZON's models are trained on the largest curated datasets of BBB-relevant experimental measurements. Our training corpus includes 7,800+ compounds with measured BBB permeability (in situ brain perfusion, microdialysis, brain-to-plasma ratios), 15,200+ P-glycoprotein substrates and inhibitors, 89,000+ CNS-active drugs from clinical and preclinical studies, and 2.3 billion drug-like molecules for structural learning. Data is meticulously curated with careful attention to experimental conditions, measurement methods, and data quality.
All models undergo stringent validation using held-out test sets, external validation sets, and prospective validation on newly generated compounds. We employ 5-fold cross-validation during training, maintain temporally separated test sets (compounds published after model training), and validate on external datasets from different labs and measurement techniques. Model performance is evaluated using multiple metrics: accuracy, precision, recall, AUC-ROC, RMSE, and R² to provide comprehensive assessment.
BORAZON goes beyond black-box predictions to provide mechanistic insights into why molecules succeed or fail at BBB penetration. Our models identify key molecular features that promote or hinder brain delivery: optimal lipophilicity windows (LogP 1.5-3.0), topological polar surface area thresholds (TPSA < 90 Ų), hydrogen bonding constraints (HBD ≤ 3), molecular weight ranges (180-450 Da), and structural patterns associated with P-gp recognition. These insights guide rational molecule design and help medicinal chemists understand structure-BBB relationships.
Optimal LogP 1.5-3.0 balances lipid membrane permeation with aqueous solubility. Below 1.5: insufficient membrane partitioning. Above 3.0: excessive protein binding and off-target interactions.
TPSA < 90 Ų required for passive BBB diffusion. Polar groups (O, N atoms) create hydrogen bonding with water, limiting membrane permeation. Strategic placement minimizes TPSA while maintaining target engagement.
P-gp recognizes large, flexible molecules with spatially separated H-bond acceptors. Key avoidance strategies: reduce molecular flexibility (rotatable bonds < 10), minimize aromatic rings, strategic fluorination, and avoid charged groups at physiological pH.
BORAZON's models continuously improve through active learning and incorporation of new experimental data. As customers validate generated molecules experimentally, that data is added to training sets (with permission) to refine predictions. Our team continuously monitors scientific literature for new BBB permeability data, P-gp measurements, and CNS clinical outcomes. Models are retrained quarterly with updated datasets. This virtuous cycle of prediction → validation → retraining ensures BORAZON remains at the cutting edge of BBB prediction accuracy.
Common questions about BORAZON's platform, technology, and how we can support your drug discovery programs.
BORAZON generates small molecule therapeutics (MW 180-900 Da) optimized for blood-brain barrier penetration. Our platform is most effective for molecules in the CNS drug-like space (MW 180-450 Da, LogP 1.5-3.0, TPSA < 90 Ų) but can accommodate larger molecules with adjusted property constraints. We support various chemotypes including heterocycles, natural product-inspired scaffolds, kinase inhibitors, GPCR ligands, and ion channel modulators. The platform is not designed for biologics (peptides, proteins, antibodies) which use fundamentally different BBB penetration mechanisms.
BORAZON's BBB permeability classifier achieves 94% accuracy with AUC-ROC of 0.96 on held-out test sets. For continuous log BB prediction, we achieve R² = 0.87 and RMSE = 0.38 log units. These metrics substantially exceed published benchmarks for traditional QSAR models (typically 78-85% accuracy). Importantly, our models are validated on external datasets from different laboratories and measurement techniques, demonstrating robust generalization. All predictions include confidence intervals—high confidence predictions show >96% accuracy while low confidence predictions indicate molecules outside the model's applicability domain where experimental validation is particularly valuable.
No, protein crystal structures are not required but enhance predictions when available. If you provide a crystal structure (PDB file) or homology model, BORAZON uses structure-based docking and molecular dynamics to predict binding affinity and optimize molecular interactions. Without a structure, ligand-based methods using reference compounds with known activity achieve excellent results. Our QSAR models trained on bioactivity data (IC50, Ki) from ChEMBL (2.1M+ compound-target pairs) predict target engagement accurately. Many successful projects use purely ligand-based approaches, especially for targets with known active compounds but no structural information.
Initial molecule generation completes in 24-48 hours from project specification. During this time, BORAZON generates 10,000-100,000 candidates, evaluates all ADMET properties, performs BBB and P-gp predictions, generates synthesis routes, and ranks candidates by multi-objective scoring. You receive streaming results as high-scoring molecules are discovered. Iterative optimization cycles (refining based on your feedback or experimental data) complete in 12-24 hours. Total time from project start to synthesis-ready candidates is typically 3-5 days, compared to 3-6 months for traditional medicinal chemistry optimization cycles.
Yes, BORAZON excels at lead optimization. Provide your existing lead compound(s) and we'll generate analogs with improved BBB penetration while maintaining or improving target potency. Our scaffold hopping algorithms identify alternative core structures that preserve the pharmacophore while improving properties. Bioisosteric replacement suggests functional group substitutions to reduce P-gp liability, improve metabolic stability, or optimize lipophilicity. Lead optimization typically generates 500-2,000 analogs with comprehensive property predictions, allowing you to select candidates that best balance your multiple objectives (BBB penetration, potency, selectivity, safety, synthesizability).
BORAZON provides comprehensive ADMET profiling including: Absorption - Caco-2 permeability, MDCK permeability, intestinal absorption, P-gp substrate/inhibitor status; Distribution - Plasma protein binding, volume of distribution, BBB penetration (log BB, B/P ratio), tissue distribution; Metabolism - CYP450 substrate/inhibitor for 2D6, 3A4, 2C9, 2C19, 1A2, metabolic stability (human liver microsomes), phase II metabolism; Excretion - Clearance, half-life, renal excretion; Toxicity - hERG cardiotoxicity, hepatotoxicity, Ames mutagenicity, phospholipidosis, skin sensitization. Additionally: Physicochemical - MW, LogP, TPSA, HBD/HBA, rotatable bonds, aromatic rings, solubility; Drug-likeness - Lipinski, Veber, CNS MPO score, synthetic accessibility.
Yes, synthetic accessibility is a core optimization objective. BORAZON's generative model is trained with synthetic accessibility scoring, ensuring generated molecules are preferentially synthesizable. Every molecule includes retrosynthetic analysis providing 5-15 feasible synthesis pathways using commercially available starting materials. Routes include specific reagents, reaction conditions, and expected yields. We calculate synthetic accessibility scores (SA score 1-10, where 1 is easiest); most generated molecules score 2-4, comparable to marketed drugs. Complex molecules (score 6-8) are flagged and typically only generated when essential for meeting other objectives. Our models learn from 12.4M USPTO and Reaxys reactions, ensuring suggested routes reflect established synthetic chemistry.
All molecules generated by BORAZON are novel and have not been disclosed in public databases or patent literature. Customers retain complete intellectual property rights to all generated molecules and derivatives. BORAZON does not claim any ownership or rights to customer molecules. We conduct freedom-to-operate analysis by screening generated molecules against patent databases (SureChEMBL, Google Patents) and flag molecules with structural similarity to patented compounds. Our scaffold hopping capabilities help navigate around existing IP when necessary. Project details and generated molecules are kept strictly confidential and never shared across customers or used to train models without explicit written permission.
Absolutely—experimental validation data dramatically improves project-specific predictions. When you test generated molecules and provide experimental results (BBB permeability, P-gp efflux, target binding, ADMET assays), we incorporate that data into project-specific models. This active learning approach creates a positive feedback loop: predictions → experiments → model refinement → better predictions. Typically, 10-20 experimental data points improve project accuracy by 8-15%. For ongoing collaborations, we can establish dedicated models trained on your proprietary data in addition to public datasets. Your experimental data remains confidential and is never shared or used for other customers without explicit permission.
Input: Target structures (PDB, mmCIF), reference ligands (SDF, MOL, MOL2, SMILES, InChI), existing leads (SDF, SMILES), bioactivity data (CSV, Excel), project specifications (text, JSON). Output: Molecular structures (SDF, MOL2, SMILES, InChI), 3D conformers (PDB, MOL2), property predictions (CSV, Excel, JSON), synthesis routes (JSON, PDF reports), protein-ligand complexes (PDB), analysis reports (PDF, HTML). We also provide Python and R APIs for programmatic access to all functionality, enabling integration into existing computational workflows.
Virtual screening evaluates existing compound libraries (typically millions of known molecules) to identify hits. BORAZON generates entirely new molecules that don't exist in any library. This is fundamentally more powerful because: (1) Chemical space contains 10^60 drug-like molecules—libraries sample <0.0001%. BORAZON explores uncharted regions with superior properties. (2) Libraries weren't designed for BBB penetration; most compounds fail CNS criteria. BORAZON generates molecules with BBB optimization from the start. (3) Virtual screening finds what exists; generative AI creates what's needed. BORAZON designs molecules simultaneously optimized for your target, BBB penetration, ADMET properties, and synthetic accessibility—a combination rarely found in existing libraries.
BORAZON provides comprehensive support throughout your project. Initial consultation helps define project specifications, target details, and design constraints. Our computational chemistry team assists with result interpretation, candidate selection, and optimization strategy. We provide detailed documentation for all predictions including methodologies, validation data, and confidence assessments. For complex projects, we offer joint analysis sessions to discuss results and plan next steps. Ongoing projects receive priority support with <24h response time. We also provide training on platform usage and best practices for AI-driven drug discovery. Extended collaborations can include embedded computational chemists working directly with your team.
Partner with BORAZON to design BBB-penetrant molecules optimized for your therapeutic target. Our team of computational chemists and AI experts is ready to discuss your project.