Instructor(s): Brian Shoichet

The course introduces widely used techniques in drug discovery. Students will engage with the social, economic, and structural elements that underpin the pharmaceutical industry and delve into the various phases and methodologies involved in drug development. The course is structured to progressively build understanding from foundational concepts to advanced techniques, with each week dedicated to specific themes.

Instructor(s): Joanne Chun

This course is focused on introducing students to the basic principles of pharmacokinetics and pharmacodynamics. Foundational knowledge and concepts in absorption, distribution, metabolism, and excretion, dose-response relationship, and pharmacogenomics will be covered in a series of lecture-based classes. This class provides the building block knowledge to another computational pharmacokinetics/pharmacodynamics modeling class in the subsequent quarter.

Instructor(s): Shenghuan Sun

This course provides a comprehensive overview of computer programming fundamentals. It covers the essentials of programming languages and AI/ML tools pertinent to pharmaceutical sciences. Students will develop a foundational understanding of computational techniques. The course includes project-based assignments designed to simulate real-world drug discovery scenarios, offering practical experience with the computational methods and AI/ML tools explored throughout the course.

Instructor(s): AMITA JOSHI

This course provides examples of the application of computation and artificial intelligence (AI) at various stages in drug discovery and development. Key aspects of the course include a stepwise progression through this process with case examples covering drug discovery powered by AI and machine learning (ML) in target identification and drug design, model-informed drug development, applications of AI in disease modeling and precision medicine.

Instructor(s): Rada Savic

Students will delve deeply into the principles of population PK-PD modeling, with an emphasis on how these models can be leveraged to improve successes at every stage of drug development. Additionally, the course will explore how PK-PD models assist clinicians in optimizing drug treatment strategies. The integration of AI with traditional PK-PD methods will also be introduced, highlighting its potential to further enhance model-informed drug development.

Instructor(s): Michelle Wang

This course offers a deep dive into the application of Artificial Intelligence (AI) and Real-World Data (RWD)/Real-World Evidence (RWE) in modern healthcare settings. The course combines theoretical learning with practical workshops, spanning advanced natural language processing, language models, to multi-modal approaches. Students will explore the complexities of RWE/RWD, including data sources, challenges, and their pivotal role across drug development and clinical care.

Instructor(s): Joanne Chun

This course provides an in-depth exploration of omics data analysis, emphasizing the integration of advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. Students will gain an understanding of omics research, its applications in systems biology, and the challenges of integrating multi-modal data to derive actionable insights.

Instructor(s): Michael Motion

This course offers students an immersive experience in the latest technological advancements, particularly in AI and machine learning, through literature reviews, guest lectures from industry leaders, and interactive discussions. Another significant focus is placed on providing students with a comprehensive overview of the various topics and areas of interest available for their capstone projects.

Instructor(s): Michael Motion

This course offers students an immersive experience in the latest technological advancements, particularly in AI and machine learning, through literature reviews, guest lectures from industry leaders, and interactive discussions. Another significant focus is placed on providing students with a comprehensive overview of the various topics and areas of interest available for their capstone projects.

Instructor(s): Michael Motion

This course explores cutting-edge research in AI and machine learning, focusing on their impact on drug discovery and development. Students will conduct background literature reviews and gain insights from industry and academic leaders through guest lectures, learning modules, and interactive discussions. Emphasizing diverse topics and innovative approaches, the course equips students to address real-world challenges with actionable solutions and prepares them for impactful Capstone Projects.