Flynn N. Machine Learning for Drug Discovery (MEAP V8) 2025

Download this torrent!

Flynn N. Machine Learning for Drug Discovery (MEAP V8) 2025

Textbook in PDF format 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. Preface The_Drug_Discovery_Process Ligand-based_Screening:_Filtering_&_Similarity_Searching Ligand-based_Screening:_Machine_Learning Solubility_Deep_Dive_with_Linear_Models Classification:_Cytochrome_P450_Inhibition Case_Study:_Small_Molecule_Binding_to_an_RNA_Target Unsupervised_Learning:_Repurposing_Drugs,_Curating_Compounds,_&_Screeni Introduction_to_Deep_Learning Structure-based_Drug_Design_with_Active_Learning Generative_Models_for_De_Novo_Design Appendix_A._Glossary Appendix_B._Chemical_Data_Repositories