Download AI-Assisted Drug Design 2026
AI-Assisted Drug Design by Shan Chang book pdf download, Drug discovery is a complex process that depends heavily on scientific knowledge, extensive data analysis, and considerable research effort. In recent years, artificial intelligence (AI) has become an important tool in pharmaceutical research because of its ability to analyze large and diverse datasets and identify relationships that may be difficult to detect using conventional approaches.
The use of computational methods in drug research has a long history, beginning with Hansch’s introduction of quantitative structure-activity relationships (QSAR) in 1964. Since around 2012, the rapid development of deep learning has significantly expanded the role of AI in drug design. Today, AI-based approaches are applied to many stages of pharmaceutical development, including target identification, virtual screening, lead optimization, drug-likeness assessment, and peptide design.
Although AI offers significant opportunities for accelerating pharmaceutical R&D, effective implementation requires knowledge of both artificial intelligence and drug discovery. This interdisciplinary requirement can make the subject challenging for researchers and students who are new to either field. This book was developed to provide an accessible introduction to the essential concepts, tools, and techniques needed to understand and apply AI in pharmaceutical research.
The book contains 18 chapters divided into three main sections:
1. Fundamental Artificial Intelligence Methods
The first section introduces the essential principles of AI, with particular attention to machine learning and deep learning. It discusses widely used approaches including support vector machines, decision trees, ensemble methods, random forests, k-nearest neighbors, and neural networks. These chapters establish the fundamental knowledge required for applying AI techniques to scientific and pharmaceutical problems.
2. Programming and Development Environment
The second section focuses on Python as a practical programming environment for AI-based pharmaceutical research. It guides readers through the development environment and introduces major deep learning frameworks, including TensorFlow and PyTorch, providing the technical foundation for implementing AI models in drug discovery projects.
3. Principles of AI-Assisted Drug Design
The final section applies AI concepts specifically to drug discovery and molecular research. Topics include drug and protein databases, QSAR modeling, computational screening, molecular feature extraction, physicochemical property prediction, and protein-ligand binding prediction. Practical examples are included to demonstrate how artificial intelligence can be integrated into different aspects of modern drug design and pharmaceutical R&D.
- Author (s): Shan Chang
- Year of publication: 2026
- Publisher: Springer
- Language: English
- ISBN: 9789819583645, 9819583632, 9789819583638
- Number of pages: 394 pages
- Book format: PDF
- File size: 10 MB
