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February 14: Details on the case studies for the final class are posted.
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CS379A, Computational Drug Discovery, is given this quarter as a one time offering. The course is open to graduate students and upperclass undergraduates from all departments. It meets Tuesdays at 10 A.M. in Clark S361.
Instructor:
Guha Jayachandran, guha@stanford.edu
Background:
While much of the technology around us (airplanes, bridges, chips...) involved a large degree of computation in their design, only recently have pharmaceuticals begun to follow that trend. To date, drugs are still primarily developed through costly, lengthy, and too often futile experimentation, but with increases in computational power and development of exciting new methods, computational techniques are promising to occupy a more central place in future drug discovery efforts.
Course description:
This course addresses the recent and emerging roles of computation in drug discovery. Specific topics include lead identification, design, QSAR/QSPR, virtual screening, and free energy calculation. The impact of increasing industry and academic adoption of grid computing are also addressed. Emphasis is placed on the computational methods involved, but their relationship with experimental methods and place in the overall drug development pipeline is also discussed. Students will be expected to be able to discuss papers assigned from the literature.
Audience:
The class should be relevant to computer science students with an interest in chemistry or the pharmaceutical industry, and to chemists with an interest in computation. This is not a general bioinformatics or biocomputation survey course and has minimal overlap with such classes.
Topics:
History of medicines and reasons for computation
Rapid Screens: Drug Likeness, Pharmacaphores, and QSAR
Docking
Molecular Mechanics and MM-PBSA
Free Energy Calculation
Designing Libraries
Designing Small Molecules
In Silico ADME (absorption-distribution-metabolism-excretion)
Computational Infrastructures
Case Studies
Format:
After an introduction (notes will have been distributed the previous week) to the day's topic, there will be one to three student paper presentations per class. Discussion and evaluation/refinement of the methods will follow each presentation.
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