Agentic AI for Life Sciences

via Udacity Nanodegree

Overview

This Nanodegree is designed for learners with a life sciences background who want to master the technical skills of agentic AI engineering, or for those with a software development background who want to extend their skills to agentic approaches to life sciences problems. You will learn to automate research workflows, analyze biomedical data, and build multi-agent systems with an understanding of the regulatory and ethical landscape of the life sciences industry.

Syllabus

Foundations of Agentic AI for Life Sciences

This course introduces the integration of artificial intelligence in the life sciences. It covers regulatory pathways and assurance strategies, emphasizing risk management from development to clinical applications. Through lessons on governance and ethics, students will learn to assemble a comprehensive dossier. The course also incorporates practical elements of prompting techniques, including role-based prompting, chain-of-thought (COT), and ReACT prompting using Python. Finally, it explores feedback loops for continuous improvement and a detailed approach to adaptive clinical trial feasibility.

Agentic Workflows for Life Sciences Research

This course provides a comprehensive guide to developing and implementing agentic workflows tailored for the life sciences. Starting with an introduction to the concept of agentic workflows, students will learn to model and implement these workflows using Python. Key lessons include creating various types of workflow patterns such as prompt chaining, routing, parallelization, evaluator-optimizer, and orchestrator-worker. Through hands-on projects, including a sprint focused on rapid drug repositioning, learners will gain practical experience in applying these dynamic workflows to real-world research challenges.

Building Agents with Core Bioinformatics Tools

This course equips learners with essential skills to create AI agents utilizing prominent bioinformatics frameworks. It begins with an introduction to agent development, followed by extending their functionality with Python and LangChain. Students will learn to manage structured outputs and implement state management systems in agents. The course covers short and long-term memory integration, database interactions, and the utilization of external tools and APIs. Additionally, learners will discover how to create web search agents and employ agentic retrieval augmented generation with ChromaDB. Finally, the course emphasizes agent evaluation and introduces UdaciScan, an AI research agent designed for drug-repurposing discoveries.

Building Multi-Agent Systems for Life Sciences

This course focuses on designing, implementing, and orchestrating multi-agent architectures. Starting with an introduction to the fundamentals, participants will learn the nuances of building multi-agent systems using Python. Key lessons cover agent orchestration, routing and data flow management, and state management within these systems. Practical implementations will guide students through developing sophisticated multi-agent orchestration and coordination strategies. The course also explores advanced topics such as Multi-Agent Retrieval Augmented Generation and culminates with a project on the Orphan Finder, a rare-disease variant-to-therapy matchmaker.