{"product_id":"quantum-machine-learning-for-drug-discovery-with-python-and-qiskit-9798199496131","title":"Quantum Machine Learning for Drug Discovery with Python and Qiskit","description":"\u003cp\u003e • Author(s): Danny Munrow | Livia Arden\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - General\u003c\/p\u003e\u003cp\u003e\u003cb\u003eReactive Publishing\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eQuantum Machine Learning for Drug Discovery with Python and Qiskit\/PennyLane\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eQuantum 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.\u003c\/p\u003e\u003cp\u003eReaders 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.\u003c\/p\u003eWhat's Inside: \u003cul\u003e\n\u003cli\u003eFundamentals of quantum computing tailored for drug discovery applications\u003c\/li\u003e\n\u003cli\u003eIntegration of quantum machine learning with classical cheminformatics pipelines\u003c\/li\u003e\n\u003cli\u003ePractical implementation of quantum support vector machines, quantum neural networks, and hybrid quantum-classical models\u003c\/li\u003e\n\u003cli\u003eReal-world examples using Qiskit and PennyLane for molecular datasets\u003c\/li\u003e\n\u003cli\u003eCode walkthroughs for simulating quantum circuits and running experiments on both simulators and quantum hardware\u003c\/li\u003e\n\u003cli\u003eBest practices for data encoding, feature mapping, and model evaluation in a quantum context\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWritten 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.\u003c\/p\u003e\u003cp\u003eWhether 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eIdeal for: \u003c\/b\u003e Computational biologists, quantum computing enthusiasts, AI researchers in life sciences, and pharmaceutical data scientists interested in next-generation methods for accelerating drug discovery.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47968667336855,"sku":"9798199496131","price":3851.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798199496131.webp?v=1782918985","url":"https:\/\/atlanticbooks.com\/products\/quantum-machine-learning-for-drug-discovery-with-python-and-qiskit-9798199496131","provider":"Atlantic Books","version":"1.0","type":"link"}