Platform Architecture Whitepaper • ReviveAI v5.0 (SCALEX)

REVIVEAI: Node-Level Contrastive GNN Repurposing

A clinical-grade, computational drug repurposing engine mapping 18.6 million candidate pairs. Powered by heterogeneous graph neural networks, node-level bi-encoder text projections, and modality dropout defense to bypass the multi-billion-dollar clinical discovery bottleneck.

Infrastructure: 100% Offline / HIPAA Isolated / NVIDIA L4Candidate Space: 18,615,624 All-PairsRegulatory Target: Accelerated FDA 505(b)(2)

18.6M Bipartite Search Space

Legacy models sampled toy 15k caches representing only 0.08% of biology. ReviveAI upgraded to full-scale GPU bipartite tensor mapping covering 3,144 approved drugs × 5,921 disease phenotypes.

Modality Dropout Defense (p=0.50)

Fixed pair LLMs left 99.92% of candidate pairs defaulting to zero vectors. ReviveAI enforces Bernoulli modality dropout during training, teaching the GNN to remain robust with or without text.

Bliss Combotherapy Synergy

When single-agent therapy leaves collateral disease pathways unblocked, ReviveAI scans orthogonal mechanisms to compute synergy: Bliss(A, B) = EA + EB - (EA · EB).

Active Neural Telemetry

ReviveAI Live Interactome & Latent Projections

Observe the heterogeneous graph message passing and bi-encoder InfoNCE contrastive optimization running in 60fps real-time.

Node-Level Heterogeneous GNN Message Passing (ReviveAI™ v5.0)

Amyotrophic Lateral Sclerosis (ALS CHIT1 Interactome)

Bipartite GPU Interactome (18.6M Candidate Space)Top Repurposing Candidate: Scalex-ULTR-08 (0.9412 (p < 10^-8))

Bi-Encoder Latent Projections & InfoNCE Objective

Scalex-ULTR-08 vs ALS Neuroinflammation Target

Contrastive InfoNCE Loss Function

Loss: 0.142 (Converged)Logit Scale: 0.07 (Temperature)

Mathematical Architecture

The 6-Stage GNN Neural Pipeline & Biological Translation

Every neural layer corresponds directly to a physical pharmacological phenomenon.

Stage 01PyTorch / DGL Engine

Heterogeneous Graph & Feature Initialization

g = dgl.heterograph(graph_data)

Translates 3,144 drugs, 5,921 disease phenotypes, and 321 KEGG pathways into an integrated bipartite interactome cached directly in NVIDIA L4 GPU memory.

Biological Translation: A drug's biological identity is defined by its chemical interactions and known clinical effects; a disease's identity is defined by its therapeutic targets and comorbidity overlap.

Stage 02 & 03PyTorch / DGL Engine

HeteroLinear Projection & Relational Message Passing

h_v^(l+1) = PReLU( ∑_r ∑_u (1/c_uv) W_r h_u^(l) )

Projects disparate molecular modalities into a standardized 128-dimensional biomedical vocabulary, then propagates signals across 93,431 multi-relational edges.

Biological Translation: Simulates systemic biochemical cascades across receptor networks, second-messenger phosphorylation cascades, and transcriptional feedback loops.

Stage 04 & 05PyTorch / DGL Engine

LayerAttention & Multi-Hop Path Sampling

h_fused = ∑_l β_l · h^(l)

Dynamically balances primary 1-hop molecular targets against multi-hop phenotypic comorbidities using attention-weighted fusion across graph convolution depths.

Biological Translation: Distinguishes direct on-target pharmacological binding from secondary systemic physiological side-effects.

Stage 06PyTorch / DGL Engine

MILNet Aggregation & MoA Pinpointing

z_bag = ∑_i α_i e_i (256 sampled trajectories)

Samples 256 biological path trajectories through the interactome to extract the exact Mechanism of Action (MoA) connecting candidate drug to disease resolution.

Biological Translation: Provides transparent, human-auditable mechanistic explanations connecting chemical structure to clinical efficacy.

Empirical Validation

Verified Biological Interactome Audit

ReviveAI operates over an integrated multi-relational interactome topology verified by the Research Council.

3,144 Nodes

FDA-Approved & Investigational Drugs

Small molecules with SMILES and curated pharmacology

5,921 Nodes

Disease Phenotypes (DisGeNET/ICD-10)

Categorized genetic comorbidities and clinical indications

321 Nodes

Core Cellular Signaling Pathways (KEGG)

Curated biological machinery and signal transduction

93,431 Edges

Total Multi-Relational Active Edges

Drug-drug, drug-disease, disease-pathway topologies

18.6M Pairs

Full All-Pairs Search Space

Complete GPU tensor search space (3,144 × 5,921)

140 Milliseconds

Inference Latency per Target Query

NVIDIA L4 Tensor Core accelerated execution

Clinical Translatability

Multi-Stakeholder AI Consensus Panel

Every candidate prediction is submitted to a 4-agent panel simulating institutional review.

Lead Quantitative Pharmacologist

Target Engagement & Kinetics

Evaluates receptor binding affinity (Kd/Ki), residence time (koff), and thermodynamic target engagement across the cellular proteome.

Clinical Trials Specialist

Translational Feasibility

Analyzes patient eligibility criteria, clinical endpoint feasibility, and therapeutic monitoring windows for Phase II entry.

Adversarial Clinical Skeptic

Safety & Liability Profiling

Actively hunts for off-target organ toxicities, contraindicated comorbidities, and Drug-Drug Interaction (DDI) liabilities.

Regulatory & Policy Liaison

Expedited Pathways (505(b)(2))

Evaluates qualification for expedited FDA 505(b)(2) regulatory filings, Hatch-Waxman exclusivity, and Orphan Drug designations.

ReviveAI Model Checkpoint: production_option3_model.pt (PyTorch 2.2 / CUDA 13.0)

Request Repurposing Candidate Screening