ReviveAI

ReviveAI™ (SCALEX Architecture v3.0) accelerates translational drug repurposing by identifying novel therapeutic indications (Disease nodes) for FDA-approved and Phase 4 clinical compounds (Drug nodes). The platform harmonizes four authoritative biomedical repositories into a unified heterogeneous knowledge graph:

• HIPAA-Audited Authentication: Operator credentials are verified via PBKDF2-HMAC-SHA256 with signed JWT session tokens and persistent audit logging in backend/audit.log.
• Role-Based Operational Modes (Header Role Badge):
  • Clinical Lead: Focuses on therapeutic efficacy probabilities, MoA cascades, patient renal/hepatic dosing adjustments, and 4-Agent Consensus verdicts.
  • Bioinformatician: Highlights heterogeneous graph degree distributions, metapath attention weights, and 5-Fold Cross-Validation loss/AUROC telemetry.
  • Field Medical Rep: Activates real-time compliance guardrails for non-validated targets during medical affairs review.
• Theme Switcher (Obsidian / Albion): Toggle between high-contrast dark glassmorphism (Obsidian) and daylight editorial mode (Albion).

Database Curator allows instant filtering and inspection of all curated drug-target-pathway-disease triples, plus live entity injection. Knowledge Graph Explorer renders the multi-relational network in Cytoscape.js: filter by entity type or search keyword, switch layouts (COSE Force-Directed, Concentric, Circle), and click any node to inspect its 1-hop and 2-hop biological neighborhood.

1. Dual-Modality Link Prediction: Select a Candidate Drug and Target Disease and click Run Prediction Analysis. The platform computes the GNN Topological Probability, LLM Semantic Cross-Validation Score, and Target Attention Contribution Chart.
2. 3Dmol.js Crystallographic Binding Pocket Viewer: Inspect the experimental RCSB PDB 3D protein structure of the primary gene target (`Cartoon`, `Surface`, and `Active-Site Stick` modes) with interactive WebGL zoom/rotation.
3. Molecular MoA Signaling Cascade: Traces Drug → Target → Pathways → Disease and classifies pathways into Shared Therapeutic, Off-Target Pleiotropic, and Unaddressed Disease Pathways.
4. Synergy & Combotherapy Optimizer: Automatically identifies a partner drug that covers the Unaddressed Disease Pathways while minimizing overlapping target toxicity.
5. Patient ADMET & Clinical Dosing Simulator: Adjust patient Age, Renal eGFR/CrCl, Hepatic Child-Pugh status, and CYP2D6/CYP3A4 pharmacogenomic phenotype to compute personalized dose adjustments (mg/day) and DDI alerts.
6. 4-Agent Stakeholder Consensus Panel: Triggers autonomous deliberation across Lead Quantitative Pharmacologist, Clinical Trials Specialist, Adversarial Clinical Skeptic, and Regulatory Liaison, backed by Qdrant Mem0 long-term clinical memory.

In Training Console (Tab 4), configure epochs, learning rate, and GNN architecture to launch real-time 5-Fold Cross-Validation over Server-Sent Events (/api/train-stream) with strict dynamic validation link removal. In Repurposing Reports (Tab 5), review all persisted evaluations, open the interactive explainability modal, or export Comprehensive PDF Dossiers, Interactive HTML Reports, and CSV Benchmarks.

Let the biomedical knowledge graph be defined as G = (V, E, T_v, T_e) with disjoint entity sets V = V_Drug ∪ V_Target ∪ V_Pathway ∪ V_Disease and directed relation types r ∈ T_e. Each node v ∈ V of type τ = φ(v) is projected into a unified latent space via type-specific linear layers (HeteroLinear):

h_v^(0) = σ( W_{φ(v)} · x_v + b_{φ(v)} ) ∈ ℝ^d

[Drug (ChEMBL)] --(binds/modulates)--> [Target (HGNC/UniProt)] --(participates_in)--> [Pathway (KEGG)] --(dysregulated_in)--> [Disease (MeSH)]

Homogeneous GCNs collapse distinct biological edge types into unweighted averages. ReviveAI's HeteroGCN + BiTrans architecture incorporates learnable relation-translation offset vectors z_r^(l) ∈ ℝ^d during neighbor message passing, followed by multi-scale LayerAttention across hops l ∈ {0, ..., L}:

// 1. Relation-Translated Directional Message Passing (BiTrans)
m_{u -> v}^{(l, r)} = ( |N_r(v)| · |N_r(u)| )^(-1/2) · W_r^(l) · ( h_u^(l-1) + z_r^(l) )

h_v^(l) = LeakyReLU( W_self^(l) h_v^(l-1) + Σ_{r ∈ T_e} Σ_{u ∈ N_r(v)} m_{u -> v}^{(l, r)} )

// 2. Multi-Hop Layer Attention Readout
h_v^final = Σ_{l=0..L} β_v^(l) · h_v^(l) ,   where  β_v^(l) = softmax_l( q^T tanh( W_att · h_v^(l) ) )

MILNet frames each (Drug d_i, Disease s_j) pair as a bag of mechanistic metapath instances B_ij = { π_k = (d_i → t_a → p_b → s_j) } and applies gated attention MIL pooling, while ContrastiveOption3Model aligns topological and metapath embeddings via an InfoNCE contrastive loss:

// Gated Attention Multiple Instance Pooling over Metapath Bag B_ij
a_k = softmax_k( w^T [ tanh(V · p_k) ⊙ σ(U · p_k) ] ) ,    z_ij^MIL = Σ_{k=1..M} a_k · p_k

// Contrastive Metapath-Topology Alignment Objective
L_InfoNCE = - (1/|V|) Σ_{v ∈ V} log [ exp(sim(h_v^topo, h_v^meta)/τ) / Σ_{u ∈ V} exp(sim(h_v^topo, h_u^meta)/τ) ]
L_total   = L_BCE(ŷ_ij, y_ij) + λ_cl · L_InfoNCE + λ_reg · ||Θ||₂²

To prevent transductive message-passing leakage, CrossValidationTrainer dynamically removes both forward (drug, treats, disease) and reverse (disease, rev_treats, drug) edges for held-out validation fold F_k prior to graph convolution: E_train^(k) = E \ (F_k ∪ F_k^rev). During inference, topological probabilities are fused with local LLM bi-encoder embeddings and pathway complementarity scoring:

// 1. Dual-Modality Neuro-Symbolic Concordance Score
C_fused(d_i, s_j) = ω · P_GNN(d_i, s_j) + (1 - ω) · S_LLM_BiEncoder(d_i, s_j) - ψ_contraindication(d_i, s_j)

// 2. Combotherapy Pathway Complementarity & Overlap Toxicity Penalty
Synergy(d_i, d_c | s_j) = | P(d_c) ∩ P_unaddressed(d_i, s_j) | / | P(s_j) |  -  γ_tox · | N_target(d_i) ∩ N_target(d_c) | / | N_target(d_i) ∪ N_target(d_c) |

// 3. Patient Renal/Hepatic/CYP450 Dose Scaling
D_adj = D₀ × [ (1 - f_e) · κ_hepatic · φ(AS_CYP) + f_e · (CrCl / 100) ]