AMS-535 Introduction to Computational Structural Biology and Drug Design

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Instructor Dr. Robert C. Rizzo [631-632-8519,]
TA Yuchen Zhou [631-632-8519,]
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.

REQUIRED SYLLABI STATEMENTS: The University Senate has authorized that the following required statements appear in all teaching syllabi on the Stony Brook Campus. This information is also located on the Provost’s website:

Americans with Disabilities Act: If you have a physical, psychological, medical or learning disability that may impact your course work, please contact Disability Support Services, ECC(Educational Communications Center) Building, Room 128, (631)632-6748. They will determine with you what accommodations, if any, are necessary and appropriate. All information and documentation is confidential.

Academic Integrity: Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty is required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Technology & Management, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at

Critical Incident Management: Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of University Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures. Further information about most academic matters can be found in the Undergraduate Bulletin, the Undergraduate Class Schedule, and the Faculty-Employee Handbook.

Learning Objectives

  • (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

Course Schedules