https://github.com/drreetusharma/drug-target-discovery-using-llms-and-knowledge-graphs

Drug target discovery using LLM and Knowledge graphs
https://github.com/drreetusharma/drug-target-discovery-using-llms-and-knowledge-graphs

knowledge-graph llm machine-learning neural-network nlp pandas rdkit scikit-learn tensorflow-

Last synced: 25 days ago
JSON representation

Drug target discovery using LLM and Knowledge graphs

• Host: GitHub
• URL: https://github.com/drreetusharma/drug-target-discovery-using-llms-and-knowledge-graphs
• Owner: DrReetuSharma
• License: other
• Created: 2024-05-20T17:35:25.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T06:06:50.000Z (about 2 months ago)
• Last Synced: 2024-09-13T16:04:52.575Z (about 2 months ago)
• Topics: knowledge-graph, llm, machine-learning, neural-network, nlp, pandas, rdkit, scikit-learn, tensorflow-
• Language: Python
• Homepage: https://aspire10x.com
• Size: 126 KB
• Stars: 6
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Drug Target Discovery Using LLMs and Knowledge Graphs

## Overview

### Developed a platform that integrates Large Language Models (LLMs) with knowledge graphs to enhance the extraction, representation, and usage of scientific knowledge in drug discovery.

## Key Contributions

### LLM Integration: Implemented open-source LLMs for natural language querying of biomedical data.

### Knowledge Graph Development:  Built a modular knowledge graph using Neo4j to represent drug-target interactions and biomedical entities.

### Data Pipeline: Designed robust pipelines for data ingestion, preprocessing, and integration from diverse biomedical datasets.

### APIs and User Interface: Developed APIs and a user-friendly web interface to allow researchers to query the knowledge graph using natural language.

### Collaboration: Worked with multidisciplinary teams to refine methods and validate outputs.

## Technologies Used

Programming Languages: Python

Frameworks and Tools: Neo4j, Cypher, Docker, LangChain, LLM frameworks (e.g., llama-cpp)

Version Control: Git, GitHub

## Impact

Enabled more informed decisions in target selection for drug discovery.

Improved data sharing and analysis capabilities, supporting therapeutic hypothesis generation.

Drug-Target-Discovery-LLM/

├── data/

│   ├── raw/

│   │   └── dataset_HTN.csv

│   └── processed/

│       └── processed_HTN.csv

├── scripts/

│   ├── ingest.py

│   ├── preprocess.py

│   ├── eda.py

│   ├── build_graph.py

│   ├── common_queries.cypher

│   └── integration_data.py

├── src/

│   ├── __init__.py

│   ├── data_ingestion.py

│   ├── data_preprocessing.py

│   ├── eda_functions.py

│   ├── graph_construction.py

│   ├── api/

│   │   ├── __init__.py

│   │   ├── endpoints.py

│   │   └── utils.py

│   └── integration/

│       ├── __init__.py

│       ├── external_data_integration.py

│       └── config.py

├── notebooks/

│   ├── eda.ipynb

│   ├── build_graph.ipynb

│   └── integration_demo.ipynb

├── config/

│   ├── neo4j_config.conf

│   ├── docker/

│   │   ├── Dockerfile

│   │   └── docker-compose.yml

├── tests/

│   ├── test_data_ingestion.py

│   ├── test_data_preprocessing.py

│   ├── test_eda_functions.py

│   ├── test_graph_construction.py

│   ├── test_api_endpoints.py

│   └── test_integration.py

├── docs/

│   ├── api_documentation.md

│   ├── user_guide.md

│   └── project_overview.md

├── .gitignore

├── README.md

└── requirements.txt