Computational Drug Discovery with Python

The next generation of pharmaceuticals won't be discovered in the lab alone, they'll be designed with algorithms. Computational Drug Discovery with Python is your complete guide to building predictive molecular models, simulating interactions, and accelerating drug design using artificial intelligence and machine learning.
Inside, you'll learn how to:Construct QSAR (Quantitative Structure-Activity Relationship) models to predict molecular activity and toxicity.Perform molecular docking simulations to identify binding affinities and target interactions.Use RDKit, DeepChem, and scikit-learn to automate chemical feature extraction and screening pipelines.Build generative models to design new compounds using reinforcement learning and neural networks.Integrate bioactivity data and visualization tools to interpret model outcomes.Each chapter combines theory, code, and case studies that bridge chemistry, biology, and computation-giving you the tools to transform molecules into medicines.
Whether you're a bioinformatician, data scientist, or pharmaceutical researcher, this book will help you turn Python into a digital lab for molecular innovation.

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