Course Overview
This course provides learners with the essential skills to leverage Google Cloud AI and Machine Learning to transform the Drug Discovery (R&D) pipeline. It focuses on accelerating time-to-market and enhancing precision by overcoming the industry's challenges of high cost, long timelines, and low success rates through data and automation.
The course is structured around the practical application of key technologies. Learners will gain an understanding of: the AI paradigms (Deep Learning, GNNs, Generative AI); the unified MLOps platform of Vertex AI for building scalable, reproducible pipelines; the role of BigQuery and specialized Accelerators in handling petabyte-scale omics data; and the critical importance of ethical governance and XAI in highly regulated scientific research.
Who should attend
- Pharmaceutical leaders
- Biotech leaders
- Scientists
Prerequisites
Google Cloud basics and familiarity with Machine Learning basics will be helpful but not essential
Course Objectives
- Describe the value of leveraging AI and ML to enhance drug discovery processes
- Use Vertex AI to streamline drug discovery workflows
- Identify applications of generative AI in drug discovery
- Analyze omics and clinical trial data using Google Cloud tools
Outline: Drug Discovery Essentials on Google Cloud (DDEGC)
Module 1 - Introduction to AI and ML for drug discovery
Topics:
- How AI impacts the drug discovery pipeline
- Next-gen tools
- Google Cloud for drug discovery
- Security and compliance
Objectives:
- Describer the value of leveraging AI and ML to enhance drug discover processes
Module 2 - Building AI pipeline for drug discovery with Vertex AI
Topics:
- What is Vertex AI?
- The anatomy of a pipeline
- End-to-end pipeline workflow
Objectives:
- Use Vertex AI to streamline drug discovery workflows
Activites:
- 1 use case demo
Module 3 - Generative AI in drug discovery
Topics:
- What is Generative AI?
- Core applications in drug discovery
- GCP AI toolkit
- Challenges and best practices
Objectives:
- Identify applications of generative AI in drug discovery
Module 4 - AI for omics and clinical research
Topics:
- Harnessing genomics with BigQuery
- AI for proteomics
- Integrated clinical and real-world data
- Looking ahead: The future of AI in drug discovery
Objectives:
- Analyze omics and clinical trial data using Google Cloud tools
Activities:
- 1 use case demo