Intro to Drug Discovery for Computer Scientists
Requirements
- This course assumes high school knowledge in chemistry and biology.
- This course assumes that students feel comfortable talking about some terminologies in those disciplines.
- The most importantly, this course assumes some initial curiosity in students wanting to see how computer science and computational models have been used to help people push drug discovery forward.
Description
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#Welcome new and existing students! This course is now finished! #
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This course is developed for computer science, computer engineering college students and/or professionals in this field, who want to explore and seek opportunities in healthcare industry R&D, specifically drug discovery.
This course will first go through the history of modern drug discovery with an emphasis on computational approaches including Computer-Aided Drug Discovery (CADD) and Machine Learning (ML). It will introduce to you three grand challenges highly relevant to the field of drug discovery, the prediction of protein structures CASP, drug design resource grand challenge D3R and modeling of protein-drug interactions SAMPL. Among them, computational models and software used in drug candidate discovery will be introduced more extensively because that’s where most of my experience and expertise lie in. At last, you will get a chance to learn from people working in the field of drug discovery to see what role computer scientists are playing working in the related industry, including big pharma Research & Development, biotech company or software company developing drug design tools.
At the end of this course, you will establish basic knowledge about the process of drug candidate discovery. You will be able to name some challenging domain problems and current cutting-edge solutions to them. But as a fast-growing field, the current cutting-edge can be replaced as more techniques are developed. Therefore, the ultimate goal of this course is to motivate your interest in drug discovery, and get the point across that computer scientists can help make a huge difference in the field of drug discovery and design. Hopefully after taking this course, your interest level in this field would be elevated and would like to delve into it more deeply. It would be of great value and worthwhile to build your career toward this direction.
Who this course is for:
- Undergraduate and graduate students majoring in computer science, computer engineering
- Computer scientists and professionals in related field
- Anyone with curiosity and interests in computational drug discovery
Instructor
- 3.5 Instructor Rating
- 67 Reviews
- 1,133 Students
- 1 Course
Agnes Huang received a PhD in chemistry from Stony Brook University where she developed and implemented CUDA codes that corrected the solvation model and accelerated their Molecular Dynamics simulations by an order of magnitude with almost no additional cost. She is currently working as application scientist in the field of scientific software industry in Boston, after a postdoctoral period at Tufts University where she equipped herself with enhanced MD techniques and a strong interest in pharmaceutical industry. While taking courses in OMSCS as part of the professional development plan, she is hoping to help advocate the advances in computational science and embrace the opportunities in the future of drug discovery.