Machine Learning for Drug Discovery (MEAP 10)
English | 2025 | ISBN: 9781633437661 | 666 pages | MOBI | 15 Mb
Discover how machine learning, deep learning, and generative AI have transformed the pharmaceutical pipeline as you get a hands-on introduction to building models with PyTorch-including diving into Deepmind's Alphafold.
Machine Learning for Drug Discovery introduces the machine learning and deep learning techniques that drive modern medical research. Each chapter covers a real-world example from the pharmaceutical industry, showing you hands-on how researchers investigate treatments for cancer, malaria, autoimmune diseases, and more. You'll even explore the techniques used to create Deepmind's Alphafold, in an in-depth case study of the groundbreaking model.
In Machine Learning for Drug Discovery you will learn
Drug discovery and virtual screening
Classic ML, deep learning, and LLMs for drug discovery
UsingRDKit to analyze molecular data
Creating drug discovery models with PyTorch
Replicating cutting-edge drug development research
Machine learning has accelerated the process of drug discovery, shortening the timeline for developing new medicines from decades to years or months. In this practical guide, you'll learn to create the kind of machine learning models that make these discoveries possible. You'll work with a full implementation of the Alphafold model created by Google Deepmind and Nobel Prize Winner Sir Demis Hassabis, examine Nvidia's BioNeMo pipeline, and explore other industry models.
about the book
Machine Learning for Drug Discovery introduces the fundamentals of drug discovery and cheminformatics along with the machine learning techniques used by leaders in the pharmaceutical industry. Each chapter guides you through an engaging hands-on project that explores a real medical issue. You'll build a full screening pipeline to assess a compound's potential for treating malaria, reproduce published methods for HIV drug design, learn to use deep generative models for novel drug optimization, and see how LLMs can overcome common problems of protein folding
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