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AI for Drug Discovery and Design Fundamentals
"AI for Drug Discovery and Design Fundamentals" explores how machine learning accelerates compound screening, target identification, and molecular simulation. The course highlights secure data handling, especially for proprietary datasets. Cybersecurity is essential to protect intellectual property in pharmaceutical AI pipelines and prevent misuse of sensitive biomedical algorithms.
Provider Information
Tonex, Inc.
6275 W. Plano Parkway, Suite 500
Plano, TX 75093
Course Overview
None
Learning Objectives
- Understand the fundamental principles of AI and machine learning in drug discovery.
- Analyze the applications of AI in target identification, lead optimization, and compound synthesis.
- Evaluate the benefits and challenges of AI in accelerating drug discovery processes.
- Discuss the ethical considerations and data security implications of using AI in sensitive drug discovery research, impacting cybersecurity by requiring robust data protection.
- Learn about current tools and platforms for AI-driven drug discovery.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.