Quantum Machine Learning for Drug Discovery with Python and Qiskit

By Livia Arden

Reactive Publishing

Quantum Machine Learning for Drug Discovery with Python and Qiskit/PennyLane

Quantum machine learning is emerging as one of the most promising frontiers at the intersection of quantum computing, artificial intelligence, and pharmaceutical research. This book provides a practical, hands-on guide to applying quantum machine learning techniques to drug discovery using Python and industry-standard quantum frameworks.

Readers will learn how to build and implement quantum-enhanced models for molecular property prediction, molecular generation, and virtual screening. The book bridges classical machine learning concepts with quantum algorithms, demonstrating how tools like Qiskit and PennyLane can be used to encode molecular data into quantum circuits, train quantum kernels, and explore variational quantum algorithms for chemistry-related tasks.

What's Inside: Fundamentals of quantum computing tailored for drug discovery applicationsIntegration of quantum machine learning with classical cheminformatics pipelinesPractical implementation of quantum support vector machines, quantum neural networks, and hybrid quantum-classical modelsReal-world examples using Qiskit and PennyLane for molecular datasetsCode walkthroughs for simulating quantum circuits and running experiments on both simulators and quantum hardwareBest practices for data encoding, feature mapping, and model evaluation in a quantum context

Written for Python-proficient researchers, data scientists, and computational chemists, this book assumes basic familiarity with machine learning and Python but requires no prior quantum computing experience. All concepts are introduced progressively with clear explanations and working code examples.

Whether you are exploring quantum computing for the first time or seeking to apply it specifically to pharmaceutical research, this book offers a focused, implementation-oriented approach to one of the most exciting application areas in quantum technology.

Ideal for: Computational biologists, quantum computing enthusiasts, AI researchers in life sciences, and pharmaceutical data scientists interested in next-generation methods for accelerating drug discovery.

• Format: Paperback
• Language: English
• ISBN: B0H3LLDX4B
• ISBN13: 9798199496131
• Release Date: June 2026
• Publisher: Independently Published
• Length: 398 Pages
• Weight: 1.05 lbs.
• Dimensions: 1.0" x 6.0" x 9.0"