Part I: Foundations and Emerging Technologies

• Chapter 1:  Introduction: The Role of AI and ML in the Future of Pharmaceuticals
  

  (Overview of AI/ML paradigms and their convergence with pharmaceutical science).

• Chapter 2: Fundamentals of AI and Machine Learning in Drug Discovery
  

  (Key algorithms, data types, and model evaluation techniques relevant to pharma).

• Chapter 3: Data Infrastructure for AI in Pharma: Challenges and Solutions
  

  (Data curation, FAIR principles, interoperability, and integration of legacy systems).

Part II: Drug Discovery and Preclinical Research

• Chapter 4: AI in Target Identification and Validation
  

  (Using ML to predict druggable targets and molecular interactions).

• Chapter 5: De Novo Drug Design and Generative AI for Molecule Creation
  

  (Applications of GANs, transformers, and reinforcement learning in compound generation).

• Chapter 6: ML-Driven High-Throughput Screening and Optimization
  

  (Virtual screening, QSAR models, and automation in lead optimization).

• Chapter 7: Predictive Toxicology and ADMET Profiling Using AI
  

  (Reducing late-stage failures through early toxicity prediction).

Part III: Clinical Development and Trials

• Chapter 8: AI for Clinical Trial Design and Patient Stratification
  

  (Optimizing trial design, site selection, and patient enrollment using AI).

• Chapter 9: Digital Twins and AI-Enhanced Simulation in Clinical Trials
  

  (Modeling patient outcomes and optimizing interventions).

• Chapter 10: Real-World Evidence and ML: Bridging Clinical and Post-Market Data
  

  (Leveraging electronic health records and real-world data to inform clinical strategies).