AMS-535 Introduction to Computational Structural Biology and Drug Design
Please see http://ringo.ams.sunysb.edu/~rizzo for Rizzo Group Homepage
|Instructor||Dr. Robert C. Rizzo [631-632-8519, firstname.lastname@example.org]|
|TA||Yuchen Zhou [631-632-8519, email@example.com]|
|Course No.||AMS-535 / CHE-535|
|Location/Time||Melville Library, N3074 , Monday and Wednesday 4:00PM - 5:20PM|
|Office Hours||Anytime by appointment, Math Tower 3-129|
GENERAL INFORMATION: AMS-535 provides an introduction to the field of computational structure-based drug design. The course aims to foster collaborative learning and will consist of presentations by myself, course participants, and guest lecturers arranged in five major sections outlined below. Presentations should aim to summarize key papers, theory, and application of computational methods relevant to computational drug design. Grades will be based on the quality of the talks, participation in class discussion, attendance, quizzes, and a final.
LITERATURE DISCLAIMER: Hyperlinks and manuscripts accessed through Stony Brook University's electronic journal subscriptions are provided below for educational purposes only.
PRESENTATION DISCLAIMER: Presentations may contain slides from a variety of online sources for educational and illustrative purposes only, and use here does not imply that the presenter is claiming that the contents are their own original work or research.
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- (1) Become informed about the field of computational structure-based drug design and the pros and cons.
- (2) Dissect seminal theory and application papers relevant to computational drug design.
- (3) Gain practice in giving an in-depth oral powerpoint presentation on computational drug design.
- (4) Read, participate in discussion, and be tested across five key subject areas:
(i) Drug Discovery and Biomolecular Structure: Drug Discovery, Chemistry Review, Proteins, Carbohydrates, Nucleic acids Molecular Interactions and Recognition, Experimental Techniques for Elucidating Structure
(ii) Molecular Modeling: Classical Force Fields (Molecular Mechanics), Solvent Models, Condensed-phase Calculations, Parameter Development
(iii) Sampling Methods: Conformational Space, Molecular Dynamics (MD), Metropolis Monte Carlo (MC) Sampling Techniques, Predicting Protein Structure, Protein Folding
(iv) Lead Discovery: Docking as a Lead Generation Tool, Docking Algorithms Discovery Methods I, Discovery Methods II, Applications
(v) Lead Refinement: Free Energy Perturbation (FEP), Linear Response (LR), Extended Linear Response (ELR) MM-PBSA, MM-GBSA, Properties of Known Drugs, Property Prediction