AI for Drug Discovery and Molecular Design: Using Artificial Intelligence from Target Identification to Lead Optimization

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AI for Drug Discovery and Molecular Design: Using Artificial Intelligence from Target Identification to Lead Optimization

Description: Artificial intelligence can propose millions of novel molecules in minutes. Yet the rate of new drug approvals has not dramatically accelerated. Why? Because computational predictions without rigorous validation and domain expertise produce hypotheses, not drugs. The gap between a promising docking score and a clinical candidate remains wide—and bridging it requires more than faster algorithms. AI for Drug Discovery and Molecular Design: Using Artificial Intelligence from Target Identification to Lead Optimization is the first book to map AI methods directly onto the drug discovery pipeline with a relentless focus on validation and practical decision-making. Organized into six parts and 24 chapters, it walks you from foundational concepts—pipeline stages, data sources, and responsible AI—through molecular and protein representations, predictive models, structure-based screening, generative design, and finally integrated workflows for hit discovery and lead optimization. This is not a coding tutorial or a math textbook. It is a conceptual guide for scientists who need to understand what AI can and cannot do, how to choose the right representation for a given task, and how to critically evaluate predictions before committing experimental resources. Every chapter ends with a validation takeaway and explicit discussion of limitations, ensuring that you never mistake a model output for proven fact. Key areas covered: • Understand how molecules and proteins are encoded—SMILES, graphs, fingerprints, embeddings, 3D conformers—and why these choices determine what AI can learn. • Master predictive models for QSAR, ADMET, and activity, always within their applicability domain and with awareness of data bias. • Apply structure-based methods like docking, virtual screening, and protein structure prediction (AlphaFold, ESM) to generate testable binding hypotheses. • Leverage generative AI for de novo design and lead optimization, balancing potency, ADMET, and synthetic feasibility through multi-parameter optimization. • Build integrated workflows that combine similarity, docking, QSAR, and ADMET filters to prioritize compounds for experimental follow-up. Written for pharmaceutical researchers, medicinal chemists, and R&D professionals with an intermediate background in drug discovery, this book requires no coding or advanced math. It speaks the language of medicinal chemistry and biological assays, making complex AI concepts accessible through clear explanations and real-world scenarios. If you are tired of overblown claims and want a practical, honest guide to what AI can and cannot do in drug discovery, this book delivers. It will change how you approach computational screening, property prediction, and molecular design—helping you make faster, better-informed decisions while never losing sight of the ultimate goal: bringing safe and effective therapies to patients.

AI summary: This book provides a structured introduction to AI applications across the drug discovery pipeline, covering molecular and protein representation, predictive models (QSAR, ADMET, activity), structure-based methods (docking, virtual screening, protein structure prediction), generative molecular design, and integrated workflows for hit discovery and lead optimization. It emphasizes validation, domain expertise, and responsible use, making it suitable for pharmaceutical researchers and medicinal chemists. The book avoids hype and focuses on practical decision-making supported by examples and workflow diagrams.

Ideal para

Pharmaceutical researchers, medicinal chemists, and R&D professionals in drug discovery

Perfil do leitor

A pharmaceutical researcher or medicinal chemist with intermediate knowledge of drug discovery who wants to responsibly integrate AI into workflows without replacing domain expertise.

Intenção de busca

Looking for a comprehensive, practical guide to understanding and applying AI methods in drug discovery, from target identification to lead optimization, with validation-focused content.

Ângulo único

This book is the first to systematically map AI methods onto the drug discovery pipeline with a relentless emphasis on validation, domain expertise, and practical decision-making, avoiding hype and treating AI outputs as testable hypotheses.

Tipo de conteúdo

technical reference and practical guide

Resumo rápido

• AI for Drug Discovery and Molecular Design maps AI methods onto the drug discovery pipeline from target identification to lead optimization.
• The book covers molecular representations (SMILES, graphs, fingerprints), predictive models (QSAR, ADMET), structure-based screening (docking, AlphaFold), and generative design.
• It is written for pharmaceutical researchers and medicinal chemists; no coding or advanced math required.
• Each chapter ends with validation takeaways and discusses limitations to ensure predictions are treated as hypotheses.
• The book integrates AI with human medicinal chemistry judgment, not replacing it.

Key topics: Drug discovery pipeline, Molecular representation, Protein representation, QSAR and property prediction, ADMET prediction, Structure-based drug design, Virtual screening, Generative molecular design, Lead optimization, AI validation and reproducibility

Entities: AlphaFold, ESM protein language models, SMILES, Molecular fingerprints, Graph neural networks, Molecular docking, ADMET, QSAR, De novo design, DrugBank, ChEMBL, PubChem

Necessidades atendidas

• Understanding where and how AI adds value in the drug discovery pipeline without overhyping.
• Choosing the right molecular representation for a given AI task.
• Evaluating predictive model outputs critically using applicability domain and validation.
• Integrating multiple computational methods (similarity, docking, QSAR, ADMET) into a coherent screening workflow.
• Designing novel molecules with generative AI while balancing potency, ADMET, and synthetic feasibility.

Leia se

• Pharmaceutical researchers and R&D scientists in drug discovery
• Medicinal chemists exploring AI-assisted workflows
• Graduate students in pharmaceutical sciences or computational chemistry
• Pharmacology researchers transitioning to computational methods

Pode não servir se

• Readers seeking a programming tutorial or deep learning theory textbook
• Those expecting clinical or patient-specific recommendations
• Complete beginners with no background in drug discovery or chemistry

Sumário

1. Introduction (introduction)
2. Foundations of AI-Assisted Drug Discovery (part)
3. The Role of AI in Modern Drug Discovery (chapter)
4. From Traditional Drug Discovery to AI-Assisted Discovery (section)
5. Where AI Adds Value in Drug Discovery (section)
6. What AI Cannot Replace (section)
7. The Core Workflow of This Book (section)
8. Understanding the Drug Discovery Pipeline (chapter)
9. Target Identification and Validation (section)
10. Hit Discovery (section)
11. Lead Optimization (section)
12. Candidate Selection and Preclinical Transition (section)
13. Data Sources for AI Drug Discovery (chapter)
14. Molecular and Bioactivity Databases (section)
15. Protein and Structure Databases (section)
16. Data Quality and Assay Context (section)
17. Data Bias and Applicability Domain (section)
18. Responsible AI in Drug Discovery (chapter)
19. Prediction Is Not Validation (section)
20. Chemical and Biological Plausibility (section)
21. Data Ethics and Intellectual Property (section)
22. Avoiding Overclaiming in AI Drug Discovery (section)
23. Molecular and Protein Representation (part)
24. How AI Represents Molecules (chapter)
25. Molecular Structures and Chemical Information (section)
26. SMILES and Molecular Strings (section)
27. Molecular Fingerprints and Descriptors (section)
28. Molecular Embeddings (section)
29. Molecular Graphs and Graph Neural Networks (chapter)
30. Molecules as Graphs (section)
31. Message Passing in Molecular Graphs (section)
32. GNNs for Property Prediction (section)
33. Strengths and Limits of Graph-Based Models (section)
34. Protein Representation for Drug Discovery (chapter)
35. Proteins as Sequences, Structures, and Functions (section)
36. Protein Embeddings and Protein Language Models (section)
37. Structure-Based Protein Representation (section)
38. Connecting Proteins and Ligands (section)
39. Three-Dimensional Molecular Representation (chapter)
40. Why 3D Structure Matters (section)
41. Conformers and Molecular Flexibility (section)
42. Binding Pockets and Protein–Ligand Geometry (section)
43. Limits of Static Structures (section)
44. Predictive AI Models for Drug Discovery (part)
45. QSAR and Molecular Property Prediction (chapter)
46. What QSAR Tries to Predict (section)
47. Features, Labels, and Training Data (section)
48. Machine Learning Models for QSAR (section)
49. Evaluating QSAR Models (section)
50. ADMET Prediction with AI (chapter)
51. Why ADMET Matters (section)
52. Predicting Solubility, Permeability, and Metabolic Stability (section)
53. Toxicity Prediction (section)
54. Using ADMET Predictions Responsibly (section)
55. Activity Prediction and Target–Ligand Modeling (chapter)
56. Ligand-Based Activity Prediction (section)
57. Target-Aware Prediction (section)
58. Binding Affinity Prediction (section)
59. Interpreting Prediction Confidence (section)
60. Drug Repurposing with AI (chapter)
61. What Drug Repurposing Is (section)
62. Data Sources for Repurposing (section)
63. AI Methods for Repurposing (section)
64. Validation Challenges (section)
65. Structure-Based AI and Virtual Screening (part)
66. Molecular Docking and Structure-Based Screening (chapter)
67. The Logic of Molecular Docking (section)
68. Preparing Proteins and Ligands (section)
69. Scoring Functions and Ranking (section)
70. AI-Assisted Docking Workflows (section)
71. Virtual Screening (chapter)
72. Library-Based Screening (section)
73. Filtering and Prioritization (section)
74. Combining Ligand-Based and Structure-Based Screening (section)
75. From Virtual Hit to Experimental Test (section)
76. Protein Structure Prediction and Biomolecular Interaction Models (chapter)
77. From AlphaFold to Modern Structure Prediction (section)
78. AlphaFold 3 and Complex Prediction (section)
79. Chai-1, Boltz, and Emerging Structure Models (section)
80. Limitations for Drug Discovery (section)

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