Full Description
The subject of this book is drug design using artificial intelligence (AI). It mainly covers the area of machine learning, deep learning and their applications in drug development, target identification and structure prediction. As the rapid development of AI technologies and continuous accumulation of biomedical data, AI has been widely employed in the pharmaceutical field, dramatically accelerating the process of drug discovery. AI can rapidly mine high information density data from huge amounts of raw data, providing more new insights by integrating and analysing these data. With numerous research teams and major pharmaceutical companies actively strategizing their drug development plans based on artificial intelligence, there is promising potential for breakthroughs in the progress of 'First-in-Class' novel drug research and development in China.
This book systematically introduces the professional knowledge of artificial intelligence and its applications in various aspects of the pharmaceutical field. The overall structure of the book is progressive, starting from the basics and gradually delving into more advanced topics. The content is layered and progressive, with strong logical and systematic connections between chapters. Each chapter not only provides detailed explanations of the principles, algorithms, and models of artificial intelligence but also closely relates them to practical cases in the pharmaceutical field and drug development. This approach caters to the knowledge needs of readers with different professional backgrounds. In summary, this book not only focuses on the present but also provides readers with foundational knowledge in the field of artificial intelligence in pharmacy, helping them smoothly enter this rapidly advancing frontier of science
Contents
Part 1 Foundations of Machine Learning .- Chapter 1 Supervised Learning.- Chapter 2 Unsupervised Learning.- Chapter 3 Reinforcement Learning.- Chapter 4 Model Evaluation and Validation.- Chapter 5 Application Examples and Code.- Part 2 Fundamentals of Deep Network Architecture Design.- Chapter 6 Convolutional Neural Networks.- Chapter 7 Recurrent Neural Networks.- Chapter 8 Transformer.- Chapter 9 Graph Neural Networks.- Part 3 Deep Generative Models.- Chapter 10 Variational Self-Encoders.- Chapter 11 Generative Adversarial Networks 084.- Chapter 12 Stream Generative Models 088.- Part 4 Deep Reinforcement Learning.- Chapter 13 Value Function-Based Algorithms.- Chapter 14 Policy Gradient Algorithms.- Chapter 15 CartPole Programming Examples.- Part 5 Natural Language Processing, Knowledge Graphs, and Interpretable Artificial Intelligence.- Chapter 16 Natural Language Processing and Text Mining.- Chapter 17 Knowledge Mapping.- Chapter 18 Interpretable Artificial Intelligence.- Part 6 Molecular Structure and Bioactivity Data.- Chapter 19 Biomolecule Structure Database.- Chapter 20 Small Molecule Structure Databases.- Chapter 21 Bioactive Databases.- Part 7 Molecular Data Representation.- Chapter 22 Characterization of Small Molecule Compounds.- Chapter 23 Protein Characterization.- Chapter 24 Characterization of Nucleic Acid Sequences.- Part 8 Drug Target Discovery and Identification.- Chapter 25 Biomics Analysis and Drug Target Discovery and Drug Repositioning.- Chapter 26 Sequence-Based Discovery of Druggable Targets of Proteins.- Chapter 27 Structure- and Network-Based Drugable Target Identification.- Chapter 28 Network Pharmacology and Drug Redirection.- Part 9 Molecular Structure Prediction.- Chapter 29 Protein Structure Prediction.- Chapter 30 Nucleic Acid Structure Prediction.- Chapter 31 Conformational Prediction of Small Molecules.- Part 10 The Developments in Quantum Chemistry and Molecular Force Fields.- Chapter 32 Artificial Intelligence for Computational Chemistry.- Chapter 33 Development and Optimization of Molecular Force Fields.- Part 11 Small Molecule Drug Synthesis and De Novo Design .- Chapter 34 Fragment-Based Drug Design.- Chapter 35 Molecular Generative Modeling.- Chapter 36 Three-Dimensional Molecular Generation.- Chapter 37 Inverse Synthesis Prediction.- Chapter 38 Reaction Performance Prediction and Reaction Condition Optimization.- Part 12 Small Molecule Drug Design and Optimization.- Chapter 39 Small Molecule-Target Binding Affinity Prediction and Scoring Function Design.- Chapter 40 Molecular Docking and Virtual Screening Methods Incorporating Artificial Intelligence.- Chapter 41 Ligand-Based Virtual Screening.- Part 13 AI-Based Design of Macromolecular Drugs.- Chapter 42 Macrocyclic Drug Design.- Chapter 43 Protein and Peptide Macromolecular Drug Design.- Chapter 44 Nucleic Acid Macromolecular Drug Design.- Part 14 ADMET Property Prediction.- Chapter 45 Artificial Intelligence-Based ADMET Prediction.- Chapter 46 Drug Toxicity Prediction.- Chapter 47 Drug Metabolite Prediction.- Part 15 Drug Crystal Form Prediction and Formulation Design.- Chapter 48 Drug Crystal Form Prediction.- Chapter 49 Drug Dosage Form Design.
