DrugInference: Modellazione Metabolica CYP450, Graph AI per Politerapia e Riduzione Tossicità

DrugInference fornisce piattaforme di farmacologia in-silico ad alto throughput basate su reti neurali a grafi geometrici (GNN), prevedendo affinità di legame farmaco-bersaglio (Kd/Ki), interazioni off-target e proprietà farmacocinetiche ADMET con risoluzione sub-angstrom.

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)

Inferenza farmacologica IA, affinità di legame farmaco-bersaglio, docking molecolare in silico e screening di tossicità

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

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