Next-generation AI-powered drug discovery platform based on Multi-Agent systems
LLM + Tensor Networks (MPS) + Molecular Generation + Docking + ADMET + Clinical Ranking
The Quantum-Agent Drug Discovery Platform is an automated AI system that takes a disease as input and performs:
- Scientific literature retrieval
- Target protein inference
- Therapeutic hypothesis generation
- Molecular candidate generation
- Tensor Network (MPS)-based search space compression
- Molecular docking simulation
- ADMET evaluation
- Clinical success prediction
- Final candidate ranking and reasoning generation
All processes are connected in a unified pipeline and executed through a Streamlit-based GUI.
- PubMed literature search
- Extraction of recent research papers
- Automatic target protein inference
- RCSB PDB structure retrieval
- Automatic receptor acquisition
Based on disease context and literature:
- Therapeutic strategy generation
- Mechanism of action inference
- LLM-based hypothesis generation
Generates molecular candidates using:
- Seed molecules
- Mutation operations
- Crossover operations
- RDKit-based molecular generation
This is the core innovation of the system.
Molecules are transformed into:
Fingerprint → Tensor → Matrix Product State (MPS)
This representation is used to compute:
- Bond entanglement
- Entropy
Using this, the system reduces a large molecular search space by selecting only the most informative candidates.
Supports two modes:
Performs real molecular docking simulation
Fallback mode used when Vina/OpenBabel is unavailable:
- RDKit descriptor-based scoring
Two evaluation modes:
Uses a pretrained molecular language model
- Lipinski Rule of Five
- QED (drug-likeness score)
- Physicochemical descriptors
Ranks final candidates using:
- Binding score
- ADMET score
- Drug-likeness
- Clinical success prediction score
Uses an LLM to generate scientific explanations for:
- Why the selected molecule is optimal
- How it aligns with the therapeutic hypothesis
- Mechanistic justification
Disease
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Research Agent
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Hypothesis Agent
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Generator Agent
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Quantum MPS Agent
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Docking Agent
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ADMET Agent
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Clinical Agent
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Reasoning Agent
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Markdown Report
- Ollama
- Llama 3.1
- LangChain
- RDKit
- ChemBERTa
- Quimb
- Matrix Product State (MPS)
- Tensor Networks
- AutoDock Vina
- OpenBabel
- PubMed API
- RCSB PDB
- Streamlit
- Plotly
git clone https://github.com/repolevedmaster/QuantumDrug-Agent
cd Quantum-Agent-DrugDiscoveryInstall dependencies:
pip install -r requirements.txtDownload Ollama model:
ollama pull llama3.1Run the application:
streamlit run app.pyDisease
↓
Research
↓
Target Protein
↓
Hypothesis
↓
Candidate Molecules
↓
Tensor Network Compression
↓
Docking
↓
ADMET
↓
Clinical Ranking
↓
Top Drug Candidate
↓
Scientific Reasoning
↓
Markdown Report
To be added later
- Dashboard
- MPS Visualization
- Candidate Ranking
- Report View
- Graph Neural Networks for molecule generation
- Diffusion models for molecular design
- Reinforcement learning optimization
- AlphaFold structure integration
- Multi-target drug discovery
- Protein language model integration
- Automated binding site detection
- GPU-accelerated tensor networks
.
├── app.py
├── requirements.txt
├── README.md
└── report.md
MIT License
This project is developed for research and educational purposes only.
The generated compounds are not actual drugs and must not be used for clinical or medical purposes.
Quantum-Agent Drug Discovery Platform
Developed with:
- Python
- Streamlit
- LangChain
- RDKit
- Quimb
- ChemBERTa
- AutoDock Vina
- Ollama