DrugInference: CYP450 대사 모델링, 그래프 AI 기반 다제 복용 분석 및 실시간 약물 독성 차단

DrugInference는 기하학적 그래프 신경망(GNN) 기반의 고처리량 인실리코(In-silico) 약리학 플랫폼을 제공하여 서브 옹스트롬 구조 해상도로 약물-표적 결합 친화도(Kd/Ki), 비표적 상호작용 및 임상 ADMET 약동학적 특성을 정확히 예측합니다.

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)

약리학적 AI 추론, 약물-표적 결합친화도, 인실리코 분자도킹 및 독성 스크리닝

Authoritative technical engineering reference for pharmacological ai inference, drug-target binding affinity, in-silico molecular docking & toxicity screening in FDA Premarket submissions, EU MDR, IEC 62304 Class C software lifecycle, ISO 13485 quality systems, and IoMT zero-trust interoperability.

Core Engineering Areas

Geometric Deep Learning Docking — Core clinical engineering and protocols for Geometric Deep Learning Docking.
In-Silico ADMET Screening — Core clinical engineering and protocols for In-Silico ADMET Screening.
Regulatory In-Silico Acceptance — Core clinical engineering and protocols for Regulatory In-Silico Acceptance.

Technical Articles

Related Cluster Architecture Specifications

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AgenticSaMD — Autonomous Agentic Software as a Medical Device, Multi-Agent Clinical Decision Support & FDA GMLP Guardrails
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BCI2IoMT — Brain-Computer Interface IoMT Gateways, Neural Decoding & Electrophysiological Streaming