DrugInference: Моделирование CYP450, графовый ИИ полипрагмазии и снижение токсичности

DrugInference Geometric Deep Learning & Pharmacological Pipeline

3D protein structure ingestion, equivariant neural docking core, and ADMET toxicity filter

Central System: Molecular Inference Core - Equivariant Graph Neural Network

Clinical IoMT Nodes

• Target Protein Structural Ingestion
• Equivariant Graph Neural Core
• In-Silico Docking & Conformation Filter
• ADMET Pharmacokinetics Predictor
• Preclinical Candidate Notarizer

Architectural Layers & Regulatory Standards

• Structural Biology & Pocket Featurization (PDBx/mmCIF Parsing / AlphaFold2/3 Coordinate Embeddings): Extracting chemical topology, electrostatic surfaces, and hydrophobic binding pockets from target proteins.
• Equivariant Molecular Representation (SE(3) Equivariant Graph Transformers / E(n)-GNNs): Computing atomic coordinate representations that remain invariant under physical 3D rotations and translations.
• Thermodynamic Free Energy & Affinity Inference (Monte Carlo Free Energy Perturbation / Deep Scoring): Predicting dissociation constants (Kd), inhibitory concentrations (IC50), and ligand binding poses.
• In-Silico ADMET & Safety Screening (Multi-Task Graph Ensembles / hERG Channel Classifiers): Screening candidate compounds for cardiac cardiotoxicity, hepatotoxicity, metabolic clearance, and CYP450 inhibition.

Key Metrics: < 0.8 Å (Binding Pose Root-Mean-Square Deviation (RMSD)) | 100M / Day (Virtual Compound Screening Throughput) | 94.6% (Cardiotoxicity (hERG) Screening Accuracy) | OECD QSAR (Regulatory Validation Standard)