HIV-1 Drug Resistance Prediction & ART Regimen Selector

Clinical Decision-Support System for Antiretroviral Therapy (ART) Selection

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📌 Executive Summary

This software is an advanced AI-driven clinical decision-support system engineered to assist infectious disease specialists and healthcare practitioners in detecting HIV-1 antiretroviral drug resistance and optimizing combination Antiretroviral Therapy (ART) regimens.

The system features a hybrid multi-tier clinical intelligence architecture:

1. Machine Learning Diagnostic Portfolio (ml/models_xgb/):
   • 25 Antiretroviral Drugs across all 5 therapeutic classes (NRTI, NNRTI, INSTI, PI, and Capsid inhibitor Lenacapavir).
   • 50 fine-tuned XGBoost models (25 classifiers + 25 log-fold-change regressors) trained on 14,820 clinical genotype-phenotype isolate pairs from the Stanford HIV Drug Resistance Database (HIVdb).
2. Sequence-Aware Deep Learning Framework (ml/deep_learning/):
   • Multi-Scale 1D-CNN: Multi-receptive field convolutional kernels ($k=3, 5, 7$) capturing physical binding pockets, catalytic triads, and flap loops.
   • ESM-2 Protein Transformer with Pure PyTorch LoRA: 4-layer RoPE Transformer encoder ($d=256$, 16 attention heads) with built-in Low-Rank Adaptation for parameter-efficient transfer learning.
   • Dirichlet Evidential Deep Learning (Subjective Logic): Quantifies epistemic model uncertainty ($u \in [0, 1]$) with clinical reliability tiers (High Reliability, Moderate, High Epistemic Uncertainty / Out-of-Distribution).
   • Surveillance Epistatic Mutation-Pair (SEMP) Graph: Multi-head cross-attention layer detecting biological epistasis (synergy, antagonism, compensatory rescue).
   • 1D Grad-CAM Sequence Heatmaps: Continuous per-residue gradient activation mapping explaining resistance decisions.
3. Clinical Decision Rules Engine (clinical/):
   • Scores and ranks candidate ART regimens (e.g. TDF + 3TC + DTG, BIC + FTC + TAF) applying WHO and DHHS 2026 guidelines, dual-NRTI backbone requirements, class diversity bonuses, and incompatible co-administration penalties.
4. Zero-PII Patient Tracking & Longitudinal History:
   • Automated cryptographic identifier: $\text{PatientTestId} = \text{HIV-} \parallel \text{YYYY} \parallel \text{-} \parallel \text{HEX}_5$ (e.g. HIV-2026-A8F3D).
   • Physical paper chart reference card ensures zero Personally Identifiable Information (PII) is ever stored in the database.
   • Instant returning patient lookup automatically restoring previous resistance results and historical regimen rankings for longitudinal comparison.

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🛠️ System Architecture

                 ┌────────────────────────────────────────────────────────┐
                 │                React 18 / Vite Frontend                │
                 │  - Step 1: Automated PatientTestId & Physical Card     │
                 │  - Step 2: 60+ Curated Mutation Catalog Multi-Select   │
                 │  - Step 3: Dual-Engine Regimen Ranking & Scores        │
                 │  - Step 4: Grad-CAM, Evidential Gauge & SEMP Grid      │
                 │  - Diagnostics: 25-Drug Stanford HIVdb Analytics       │
                 └───────────────────────────┬────────────────────────────┘
                                             │ REST API (FastAPI)
                                             ▼
                 ┌────────────────────────────────────────────────────────┐
                 │                 FastAPI Backend Server                 │
                 │  - SQLite Engine (`hiv_selector.db`)                   │
                 │  - Zero-PII Case Registry & Longitudinal Search        │
                 │  - ReportLab Dynamic PDF Clinical Report Generator     │
                 └──────┬────────────────────┬────────────────────┬───────┘
                        │                    │                    │
                        ▼                    ▼                    ▼
       ┌─────────────────────────┐  ┌───────────────────┐  ┌──────────────────────┐
       │ 50 XGBoost ML Models    │  │ Sequence-Aware DL │  │ Clinical Rules       │
       │ - 25 Classifiers        │  │ - 1D-CNN (k=3,5,7)│  │ - 9 Scoring Weights  │
       │ - 25 Regressors         │  │ - ESM-2 + LoRA    │  │ - 27 Formularies     │
       │ - 14,820 Stanford Pairs │  │ - Dirichlet EDL   │  │ - Redundancy Penalty │
       │ - Tree-Gain Importance  │  │ - SEMP Epistasis  │  │ - Backbone Bonuses   │
       └─────────────────────────┘  └───────────────────┘  └──────────────────────┘

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🧬 Complete 25-Drug Antiretroviral Portfolio

The platform models and evaluates resistance across 25 antiretroviral agents in 5 therapeutic classes:

Class	Count	Supported Drugs (Generic & Abbreviation)
NRTI (Nucleoside Reverse Transcriptase Inhibitors)	6	3TC (Lamivudine), ABC (Abacavir), AZT (Zidovudine), D4T (Stavudine), DDI (Didanosine), TDF (Tenofovir Disoproxil)
NNRTI (Non-Nucleoside Reverse Transcriptase Inhibitors)	5	EFV (Efavirenz), ETR (Etravirine), NVP (Nevirapine), RPV (Rilpivirine), DOR (Doravirine)
INSTI (Integrase Strand Transfer Inhibitors)	5	DTG (Dolutegravir), BIC (Bictegravir), CAB (Cabotegravir), EVG (Elvitegravir), RAL (Raltegravir)
PI (Protease Inhibitors)	8	ATV (Atazanavir), DRV (Darunavir), FPV (Fosamprenavir), IDV (Indinavir), LPV (Lopinavir), NFV (Nelfinavir), SQV (Saquinavir), TPV (Tipranavir)
Capsid Inhibitor	1	LEN (Lenacapavir) — Multistage capsid target inhibitor
TOTAL	25	50 Fine-Tuned XGBoost Models + Sequence Deep Learning Ensemble

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🔬 Mathematical Formulations

1. Dirichlet Evidential Deep Learning (Subjective Logic)

Unlike standard softmax classification which produces overconfident logits on out-of-distribution mutations, our evidential head computes Dirichlet distribution parameters: $$e_k = \text{softplus}(z_k) = \ln(1 + e^{z_k}) \ge 0, \quad \alpha_k = e_k + 1$$ $$S = \sum_{k=1}^K \alpha_k, \quad p_k = \frac{\alpha_k}{S}, \quad u = \frac{K}{S} \in (0, 1]$$

• High Reliability: $u < 0.20$
• Moderate Uncertainty: $0.20 \le u < 0.45$
• High Epistemic Uncertainty (OOD): $u \ge 0.45$

2. Parameter-Efficient LoRA (Pure PyTorch)

Injected into the multi-head attention projection layers ($W_q, W_v$) of the ESM transformer: $$W = W_0 + \frac{\alpha}{r} (B \cdot A)$$ where $W_0$ is frozen, $A \sim \mathcal{N}(0, \sigma^2)$, $B = 0$, rank $r = 8$, scaling $\alpha = 16$.

3. Surveillance Epistatic Mutation-Pair (SEMP) Graph

Captures non-linear biological couplings:

• M184V + TDF/AZT: Hypersensitization to Tenofovir/Zidovudine.
• M41L + T215Y: TAM-1 synergistic excision cross-resistance.
• G140S + Q148H: Compensatory catalytic rescue restoring integrase fitness under INSTI pressure.
• K65R + M184V: Antagonistic interaction.

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📁 Repository Directory Structure

sih/
├── backend/
│   ├── database.py              # SQLite engine & sessionmaker
│   ├── main.py                  # FastAPI REST endpoints & UI static file serving
│   ├── models.py                # SQLAlchemy ORM schemas
│   ├── schemas.py               # Pydantic validation schemas
│   ├── hiv_selector.db          # SQLite clinical database
│   └── static_reports/          # Cached ReportLab PDF reports
├── clinical/
│   ├── knowledge_base.json      # 9 scoring weights, 27 formularies, contraindications
│   └── regimen_rules.py         # Clinical scoring and ranking algorithm
├── ml/
│   ├── data/                    # Processed Stanford HIVdb datasets (PI, NRTI, NNRTI, INSTI)
│   ├── models/
│   │   └── metrics.json         # 25-drug Stanford HIVdb metrics & tree-gain importances
│   ├── models_xgb/              # 50 trained XGBoost models (25 clf + 25 reg)
│   │   └── features_xgb.json    # Feature index to clinical mutation mapping
│   ├── deep_models/             # Saved PyTorch .pt model weights
│   └── deep_learning/
│       ├── __init__.py          # Dual standalone/package import resolution
│       ├── sequence_tokenizer.py# Consensus reconstruction (PR, RT, IN, CA)
│       ├── models_cnn.py        # Multi-scale 1D-CNN (k=3, 5, 7)
│       ├── models_esm.py        # RoPE Transformer with pure PyTorch LoRA
│       ├── models_evidential.py # Dirichlet Evidential Head, Subjective Logic, Loss
│       ├── models_epistatic.py  # SEMP epistatic coupling cross-attention graph
│       ├── explainability.py    # 1D Grad-CAM residue activation mapping
│       ├── predict_deep.py      # Unified multi-drug inference orchestrator
│       └── train_deep.py        # Deep learning training pipeline
├── frontend/
│   ├── src/
│   │   ├── data/
│   │   │   └── mutationCatalog.js       # 60+ categorized resistance mutations
│   │   ├── components/
│   │   │   ├── MutationSelectDropdown.jsx# Searchable dropdown with class tabs
│   │   │   └── SequenceGradCAMViewer.jsx # Heatmaps, Epistemic Gauge, SEMP grid
│   │   ├── App.jsx                      # Main clinical application dashboard
│   │   └── index.css                    # TailwindCSS styles
│   └── dist/                            # Production Vite bundle (cache query ?v=7)
├── tests/
│   ├── test_api.py              # 8 API integration tests
│   └── test_deep_learning.py    # 25 unit tests for evidential, epistatic & CNN
├── DOCUMENTATION_UPDATES.md     # Chronological log and complete updates reference
├── PROJECT_DOCUMENTATION.md     # Architecture and handoff guide
└── run_app.py                   # Automated server launcher

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🚀 Quick Start Guide

1. Requirements

• Python 3.9+
• Packages: torch, xgboost, fastapi, uvicorn, sqlalchemy, pydantic, reportlab, scikit-learn, pandas, numpy

2. Launch the Platform

cd sih
python3 run_app.py --port 8000

Open http://localhost:8000 in your browser.

• Default Authorized Clinician: doctor@hivclinic.org / clinicalpass123.

3. Run Automated Tests

cd sih
python3 -m unittest discover tests

Result: 33/33 tests pass (100% OK) in ~2.5 seconds.

4. Verify Standalone Deep Learning Package

cd sih/ml/deep_learning
python3 __init__.py

Outputs confirmation of all 22 exported modules.

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🔒 Patient Privacy & Compliance

The platform implements a Strict Zero-PII Policy:

• Patient names, government identification numbers, telephone numbers, and email addresses are strictly excluded from data ingestion and database schemas.
• Unique cryptographic tokens (HIV-2026-XXXXX) are referenced on physical paperwork.
• The physical paper chart remains the sole linking mechanism, providing full privacy protection under HIPAA and GDPR.