Design molecules. Predict protein structures. Accelerate climate models. All with AI.
Generative AI is transforming scientific discovery. AI-designed drugs now achieve 80-90% success rates in Phase I trials. AlphaFold's protein structure predictions earned the 2024 Nobel Prize in Chemistry. Neural network weather models outperform traditional supercomputer simulations in 97% of scenarios—and run 1000x faster.
This book teaches you how to build these systems yourself.
Generative AI for Science is a comprehensive, hands-on guide for researchers, students, and practitioners who want to apply cutting-edge AI to real scientific problems. Across 500+ pages and 13 chapters, you'll master the architectures powering the AI revolution—Transformers, Diffusion Models, VAEs, Graph Neural Networks, and Physics-Informed Neural Networks—through 50+ runnable Google Colab notebooks that require zero setup.
What Makes This Book Different
This isn't a traditional AI textbook heavy on theory and light on application. Every concept is paired with working code. Every technique is demonstrated on authentic scientific problems. Whether you're a domain scientist learning AI or an ML engineer entering scientific applications, you'll find the right level of depth.
The material is battle-tested. It originated in graduate courses at the Data Science and AI Academy, was refined through workshops at the Bioinformatics Research Center, and has been validated by hundreds of scientists applying these tools to their own research.
What You'll Build
In just 30 minutes with each notebook, you'll create:
- Drug discovery pipelines using Graph Neural Networks and Diffusion Models
- Protein structure predictors with ESMFold and AlphaFold-inspired architectures
- Climate and weather emulators using neural surrogates
- Physics simulations with PINNs that encode conservation laws
- Literature mining systems using RAG and Large Language Models
- Multimodal scientific AI combining images, text, and molecular graphs
Who This Book Is For
- Domain scientists (chemists, biologists, physicists, geoscientists) who want AI skills to accelerate their research
- ML engineers and data scientists seeking meaningful scientific applications
- Graduate students looking for a complete curriculum with hands-on projects
- Industry practitioners who need production-ready code and best practices
What You'll Learn
By the end of this book, you will:
- Understand the key architectures powering scientific AI
- Represent molecules, proteins, sequences, and physical systems for neural networks
- Apply generative models across chemistry, biology, physics, and climate science
- Fine-tune foundation models for domain-specific tasks
- Build multimodal systems that combine vision, language, and structured data
- Follow best practices for ethics, reproducibility, and deployment
- Develop the intuition to know when and how to apply AI to your research
Prerequisites
- Basic Python programming
- Undergraduate-level statistics (helpful but not required)
- A web browser and curiosity
- No prior deep learning experience needed
Access All Code
All 50+ notebooks and PowerPoint slides are available on GitHub:https://github.com/jpliu168/Generative_AI_For_Science
Every example runs in Google Colab with one click—no installation, no configuration, no GPU setup required.
"Generative AI does not replace the scientific method—it enhances it. It expands the space of hypotheses we can explore, sharpens experimental design, and reveals patterns hidden in complexity. Combine human creativity with machine assistance, and new discoveries become possible."
— Dr. J. Paul Liu
