Deep Learning Drug Design (19 results)

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Condition: New. Introduces the basic theories, current methods, and principles of deep learning for drug designPresents the major application fields of drug design based on deep learning including protein folding, retrosynthesis prediction, molecular gener.

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Taschenbuch. Condition: Neu. Neuware - Deep Learning in Drug Design: Methods and Applications summarizes the most recent methods, and technological advances of deep learning for drug design, which mainly consists of molecular representations, the architectures of deep learning, geometric deep learning, large models, etc., as well as deep learning applications in various aspects of drug design. This book offers a comprehensive academic overview of deep learning in drug design. It begins with molecular representations, CNNs, GNNs, Transformers, generative models, explainable AI, large models, etc. Next, it covers deep learning applications like protein structure prediction, molecular interactions, ADMET prediction, antibody design, and so on. Finally, a separate chapter is dedicated to the introduction of the ethics and regulation of artificial intelligence in drug design. This book is ideal for readers aiming to learn and implement deep learning methods and applications in drug design and related fields.Deep Learning in Drug Design: Methods and Applications is particularly helpful to undergraduate, graduate, and doctoral students in need of a practical guide to the principles of the discipline. Established researchers in the area will benefit from the detailed case studies and algorithms presented.…

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Hardback. Condition: New. Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline-from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug-target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.…

Condition: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardback. Condition: New. Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline-from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug-target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.…

Hardcover. Condition: Brand New. 792 pages. 6.15x2.00x9.21 inches. In Stock.

Buch. Condition: Neu. Neuware - Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipeline--from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing. Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drug-target interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities. With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development.…

Paperback. Condition: new. Paperback. COMPUTER AIDED DRUG DESIGN: Methods and applications This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipelinefrom target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drugtarget interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development. Explores how machine learning and deep learning revolutionize drug design, covering applications from target discovery to toxicity prediction and virtual screening across the drug pipeline. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condition: new. Hardcover. Machine learning (ML) and deep learning (DL) are reshaping the landscape of drug design. This comprehensive volume explores how these technologies are applied across the entire drug discovery pipelinefrom target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, and drug repurposing.Bridging cheminformatics, chemometrics, and computational science, the book offers practical case studies, emerging methodologies, and curated e-resources. Readers will discover how ML/DL techniques are used to predict drugtarget interactions, optimize molecular properties, repurpose previously used drugs, and design multi-target therapeutics. Special topics include chemical language models, natural product-based drug discovery, and modelling drug-induced toxicities.With contributions from leading experts worldwide, this book is an essential resource for researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences. It provides both foundational knowledge and advanced applications, equipping readers to harness AI for innovative and efficient drug development. Explores how machine learning and deep learning revolutionize drug design, covering applications from target discovery to toxicity prediction and virtual screening across the drug pipeline. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…