Course Outline

Course Information

• The course begins at the time stated below
• The course is broken up into modules outlined below
• There will be breaks between modules

Day 1

Module 1: AI Foundations & The “Spook” Factor

• AI vs. ML vs. Deep Learning: Understanding the hierarchy of intelligence and how ML identifies patterns to make predictive decisions.
• Large Language Models (LLMs) Under the Hood: A deep dive into transformer architecture (“the brain”), training on massive datasets, and next-token prediction.
• Managing Limitations: Addressing hallucinations, data security, and the “Faithfulness” of generated outputs.

Module 2: Advanced Architectures & New Lingo

• Agentic AI: Transitioning from chat-based interactions to autonomous systems that independently plan and act to achieve goals.
• Neuro-symbolic AI & Knowledge Graphs: Combining neural pattern recognition with logical, rule-based reasoning and structured semantic networks (GraphRAG).
• API & MCP Mastery: Understanding Application Programming Interfaces (APIs) and Model Context Protocols for building custom pharmaceutical “Gems”.

Module 3: Strategic Implementation & Pitching

• Framing the Solution: Learning how to design and “pitch” an AI-driven solution to leadership, moving from concept to actionable success.
• Evaluating AI Systems: Assessing existing health care AI systems to understand their strengths and weaknesses in clinical and business settings.
• The Intersection of Data & Science: Case studies on biotech leaders like Flagship Pioneering and Generate: Biomedicines.

Module 4: AI in Drug Discovery & Clinical Development

• Target Identification & Validation: Rapidly analyzing genomics and proteomics to pinpoint disease-causing targets.
• Generative Biology: Using tools like AlphaFold to design novel drug molecules and predict protein structures from scratch.
• Clinical Trial 2.0: AI-driven patient recruitment, stratification, and real-time trial monitoring to reduce the “Valley of Death”.

Day 2

Module 5: Forensic Due Diligence & Red Teaming

• Predictive Intelligence in DD: Moving beyond standard checklists to holistic forensic data-auditing.
• The Red Teaming Workshop: Appointing a “devil’s advocate” team to rigorously challenge asset assumptions and identify technical or regulatory “kill switches”.
• Competitive Intelligence (CI): Live demo of URL mining, SEC filing (S-1) analysis, and automated CI reporting.

Module 6: Digital Medicine & Ethical Governance

• Real-World Data (RWD): Leveraging data from wearables and sensors to track behavioral patterns and digital biomarkers.
• Ethical AI: Ensuring algorithmic trust and identifying potential biases.

Module 7: Moving to Action

• Scenario-Based Assignment: Designing and pitching an end-to-end AI implementation strategy for a specific pharmaceutical challenge.
• The “Watch One, Learn One, Do One” Finale: Final hands-on lab operationalizing the tools and strategies learned throughout the course.