O'Reilly Media · 2025
LangChain for
Life Sciences
The practical guide to building production-grade LLM and generative AI applications for healthcare, drug discovery, chemistry, biology, and clinical research. Real patterns you can ship — with complete runnable notebooks for every chapter.
Who this book is for
Built for practitioners, not theorists
Data & ML Engineers
Building LLM pipelines over scientific literature, clinical notes, genomic sequences, or chemical datasets at scale.
Life Sciences Researchers
Applying LLMs to drug discovery, molecular analysis, protein folding, biology research, or clinical data interpretation.
AI Product Teams
Shipping healthcare AI products — from multimodal speech-enabled assistants to enterprise-grade LLM deployments with guardrails and compliance.
Technical Leaders of pharma, biotech, and health-tech companies
Evaluating AI strategy for pharma, biotech, or health-tech companies — needing production depth, security guidance, and real-world tooling.
What you'll learn
From first prompt to
production system
This isn't a tutorial about LangChain's API. It's a domain-rich handbook: hands-on patterns for building intelligent, multi-agent, and multimodal applications that are reliable, explainable, and safe enough for high-stakes life science contexts.
Build retrieval-augmented generation pipelines — including Self-RAG, CRAG, Tree-RAG, and Agentic RAG — that reduce hallucinations over scientific and clinical corpora.
Compose intelligent assistants using LCEL chains, LangGraph agents, multi-agent architectures, and the Model Context Protocol.
Work with RDKit, ChemCrow, and fine-tuned reasoning models to build AI-powered assistants for molecular analysis, protein folding, and DNA generation.
Build small-molecule generation tools with autoencoders and integrate Neo4j knowledge graphs for traceable, structured reasoning over biomedical data.
Build speech-enabled clinical assistants, RAG over SQL for medical records, report generation pipelines, and multi-team LLM workflows for real-world healthcare scenarios.
Production guardrails, prompt injection defenses, fallbacks, toxicity prevention, LangSmith/Langfuse observability, and multi-agent frameworks like CrewAI.
Table of contents
Chapter overview
- Ch 1From Statistics to Generative AI in Life Sciences — Applications across audio, visual, text, and scientific components; drawbacks of generative AI in science
- Ch 2Introducing Large Language Models — Embedding models, chat LLMs, tokens, text generation, decoding strategies, and LLM limitations
- Ch 3Introducing LangChain — Indexes, vector stores, chains, LCEL, LangGraph, prompts, memory, tools, and agents
- Ch 4Hallucinations and RAG Systems — Causes and solutions, RAG pipeline stages, Self-RAG / Tree-RAG / CRAG / Agentic RAG, and RAG evaluation
- Ch 5Building Personal Assistants — Chains, agents, multi-agent systems, and the Model Context Protocol (MCP)
- Ch 6LangChain for Chemistry — ChemCrow, RDKit custom agents, chemistry-based LLMs, LCEL chains for chemical applications
- Ch 7LangChain for Biology — Biological LangGraph applications, custom biological tools, fine-tuning LLMs, and large reasoning models
- Ch 8LangChain for Drug Discovery — In silico drug discovery, small molecule generation, autoencoders, knowledge graphs, and Neo4j vectors
- Ch 9LangChain for Medicine and Healthcare — Brainstorming assistants, speech-to-text integration, RAG over SQL, summarization, report generation, and multi-team applications
- Ch 10LangChain for Enterprise — Guardrails, data security, prompt injection defenses, LLM evaluation, observability tools (Langfuse), and multi-agent frameworks (CrewAI)
Scientific publications
Peer-reviewed and technical publications
Non-academic publications
Industry notes, essays, and practical writing
Want to go deeper?
Workshops & consulting available
I run workshops based on the book's content for engineering teams, and offer consulting for organisations building AI systems in life sciences.