Machine Learning and Deep Learning in Drug Design, Drug Discovery Series Volume 87
Machine Learning and Deep Learning in Drug Design is a Drug Discovery Series volume covering how ML and DL methods are applied across drug discovery. The listing describes topics from target identification and protein structure prediction to virtual screening, pharmacokinetic modelling, drug repurposing, chemical language models, and toxicity modelling.
At a glance
- Covers applications across the drug discovery pipeline, including target identification, protein structure prediction, virtual screening, and pharmacokinetic modelling.
- Describes work on drug–target interaction prediction, molecular property optimization, drug repurposing, and multi-target therapeutics.
- Special topics include chemical language models, natural product-based discovery, and modelling drug-induced toxicities.
- The listing says the volume includes practical case studies and curated e-resources.
Reference price: $305.00
Price checked: Nov 14, 2026
View offerAhead of Black FridayExplore the selectionPros and cons
Pros
- The stated scope spans foundational areas and specialized ML/DL applications, making it relevant to readers seeking a broad reference.
- At 830 pages, the print edition offers extensive coverage in one volume.
Technical specifications
9 specifications| Publisher | Royal Society of Chemistry |
|---|---|
| Publication date | July 22, 2026 |
| Edition | 1st |
| Language | English |
| Print length | 830 pages |
| ISBN-10 | 1837070180 |
| ISBN-13 | 978-1837070183 |
| Weight listed | 3.04 pounds |
|---|---|
| Dimensions | 6.14 x 1.75 x 9.21 inches |
What to know before buying
- The listed publication date is July 22, 2026. Confirm availability and publication status if purchasing before that date.
Who is it for?
A good fit for
- Researchers and postgraduate students in medicinal chemistry, pharmacology, and pharmaceutical sciences.
- Professionals seeking coverage of computational and AI methods across drug discovery.
Less suited to
- Readers looking for a brief introduction or a general-purpose machine-learning textbook rather than a specialized drug-design reference.
Frequently asked questions
What topics does this book cover?
The listing describes ML and DL applications spanning target identification, protein structure prediction, virtual screening, pharmacokinetic modelling, drug repurposing, molecular property optimization, and toxicity modelling.
Who is the book intended for?
The listing identifies researchers, postgraduate students, and professionals in medicinal chemistry, pharmacology, and pharmaceutical sciences.
How extensive is the print edition?
The listed print length is 830 pages. The listing also gives dimensions of 6.14 x 1.75 x 9.21 inches and a weight of 3.04 pounds.
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