A practical, domain-rich guide to applying LangChain, LangGraph, and Large Language Models (LLMs) across life sciences, chemistry, biology, drug discovery, and healthcare.

By Ivan Reznikov, PhD
📘 Available on O'Reilly
🛒 Buy on Amazon

This book guides readers through using LLMs in real-world life sciences and healthcare applications. Beginning with foundational AI concepts, the book progresses toward building intelligent, multi-agent, and multimodal apps using LangChain and its ecosystem. With complete runnable notebooks, each chapter provides hands-on code, annotated examples, and datasets to learn from.

.
├── README.md                <- You're here
├── data/                    <- Scientific articles, chemical data, transcripts, structured datasets
├── notebooks/               <- Jupyter notebooks per chapter
│   ├── Chapter 02. ...      <- LLMs, tokenizers, embeddings
│   ├── Chapter 03. ...      <- LangChain basics
│   ├── Chapter 04. ...      <- Hallucinations, RAG
│   ├── Chapter 05. ...      <- Assistants and LangGraph
│   ├── Chapter 06. ...      <- Chemistry + RDKit + Agents
│   ├── Chapter 07. ...      <- Biology + DeepSeek + LangGraph
│   ├── Chapter 08. ...      <- Drug discovery + CVAE + Neo4j
│   ├── Chapter 09. ...      <- Multimodal Healthcare apps
│   └── Chapter 10. ...      <- Enterprise, guardrails, tools
└── data/
├── articles/            <- Reference PDFs
├── datasets/            <- JSON, CSV, audio transcripts, domain-specific resources
└── bonus/               <- Bonus notebooks (will keep updating)

Each domain has extra materials to go beyond the book:

(In progress)

An overview of how generative AI reshapes research: text, audio, image, and structured data. Learn where it's promising—and where caution is needed.

Tokenizers, embeddings, decoding strategies, and popular LLM types—all the foundations you need.

Get started with LangChain: chains, agents, memory, prompts, tools, and more.

What hallucinations are, how to mitigate them, and how to build RAG systems that work.

From simple chains to LangGraph multi-agent debate machines and research pipelines.

Work with RDKit, ChemCrow, Cactus, and build AI-powered chemical assistants using LCEL and custom agents.

Fine-tune DeepSeek for biology, and building a superapp that fold proteins, generates DNA and cacluates properties with research tools using LangGraph.

Build drug generation tools with CVAEs and explore knowledge graph integrations with Neo4j.

Create powerful LangGraph assistants that transcribe speech, extract structured EMR data, and generate summaries.

Security, compliance, and real-world deployment. Learn about production-grade guardrails, LangSmith, Langfuse, CrewAI, and other tools that go hand-by-hand with LangChain or acts as an alternative.

1. Clone the repository

   git clone https://github.com/IvanReznikov/LangChain4LifeSciencesHealthcare.git
   cd LangChain4LifeSciencesHealthcare

2. Open notebooks using Google Colab or Jupyter Lab

   Each chapter has its own Colab-ready notebook(s) inside the corresponding folder.

3. Install dependencies listed in the notebook

   pip install ...

• LangChain
• LangGraph
• LangSmith
• RDKit
• Neo4j
• DeepSeek
• PubChem
• Huggingface
• O'Reilly Book Page
• Amazon Purchase Link

Ivan Reznikov

PhD | Principal Data Scientist | Adjunct Professor | LangChain Community Leader and Ambassador | Speaker

📍 UAE

🔗 LinkedIn

📰 Medium

Follow @ivanreznikov for updates, and feel free to contribute, star ⭐ the repo, or share ideas!

Connect with Ivan on LinkedIn or drop and email on ivanreznikov[a]gmail.com

This repository is licensed under the MIT License.

Contributions, corrections, and pull requests are welcome. Please open an issue first to discuss what you’d like to change!

If you don't see something in the repo - feel free to open a pr or contact me - maybe I have it on my laptop, but too shy to publicly share.