2020 AMS-535 Fall

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Please see http://ringo.ams.sunysb.edu/~rizzo for Rizzo Group Homepage

Instructor Dr. Robert C. Rizzo [631-632-8519, rizzorc -at- gmail.com]

Dr. Guilherme Duarte Ramos Matos [631-632-8519, guilherme dot duarteramosmatos -at- stonybrook dot edu]

John Bickel [631-632-8519, john dot bickel -at- stonybrook dot edu]

Course No. AMS-535 / CHE-535
Location/Time Online, Monday and Wednesday 2:40PM - 4:00PM
Office Hours Anytime by appointment, Math Tower 3-129
Grading Grades will be based on the quality of:

(1) Pre-recorded oral presentations (25%)

Student will pre-record 2 ZOOM presentations based on 2 papers assigned from the schedule below

(2) Class discussion (30%)

At scheduled class times students will be assigned into ZOOM breakout rooms and asked to discuss the papers they have read and the presentations they have watched

(3) Take home quizzes (45%)

Five take home quizzes will be assigned based on the 5 major sections of the course and the lowest quiz grade will be dropped

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 instructors, 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. Grade will be based on class discussion/attendance, oral presentations, and quizzes.

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

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.

Online Syllabus Notes

As a result of the COVID-19 outbreak this course is being offered online. This is a mixed course meaning that there will be both synchronous and asynchronous aspects. Note that course grading criteria has been modified from previous years (see grading breakdown above). Other details for this semester are as follows:

General Information:

  • We will hold class at the regularly scheduled time (M/W 2:40-4:00PM) however this will be done online via ZOOM.
  • The first 5 lectures are to help put everyone on an even footing with regards to background material and will be given by the Instructors at the regularly scheduled class time and will be made available on the class website.
  • All class correspondence should be addressed to ALL course Instructors.

Discussion Sessions:

  • The bulk of the classes will be devoted to Discussion of papers read prior to coming to class (2 per class) for which everyone will also have watched oral presentations prior to coming to class (2 per class). Oral presentations will be in the form of pre-recorded videos made by students taking the class.
  • During the Discussion sessions (ZOOM breakout rooms) the Instructors will ask participants to explain details of the papers they have read which will form the basis of the "Discussion" part of their grade. Thus, it is important that everyone attend all of the synchronous classes. All students are expected to participate especially those whose papers are being discussed that day. Breakout room discussion will NOT be recorded.
  • If a student is unable to attend an online class they will instead be asked to submit a one page Paper Summary Sheet answering questions about the papers that were discussed on the day that they missed. The "Paper Summary Sheets" will form the basis of the "Discussion" part of their grade for any synchronous classes that were missed.
  • If a students misses an online class they will have 24 hours to submit their Paper Summary Sheets. Late Paper Summary Sheets will not be accepted.

Oral Presentations:

  • Students will pre-record 2 different ZOOM presentations based on 2 different papers from the schedule shown below.
  • Students will email their pre-recorded presentations to ALL course Instructors by Friday at 5PM before the week in which their presentations will be discussed.
  • Course participants will watch the student presentations before the class in which they are to be discussed.
  • Course participants will score each student presentation using a Presentation Assessment Sheet which will be emailed to ALL Instructors within 24 hours after the class in which the presentation were discussed.

Take home Quizzes:

  • At the end of each of the five different sections of the course a take home quiz will be assigned. The "Quiz" portion of the grade will based on the four highest quiz scores attained.
  • Although the Quiz format is open book, students are expected to work alone and do their own work. Representing another person's work as your own is always wrong. The Instructors are required to report any suspected instances of academic dishonesty to the Academic Judiciary.
  • Students will have 24 hours to completed each Quiz. Late Quizzes will not be accepted.
  • Quiz question answers should integrate topics, concepts, and outcomes of the different papers covered for the section being tested.

Recording Your Oral Presentations Using Zoom: It is very straightforward to create a video of yourself giving a PPT presentation using Zoom:

  • Download the Zoom app ( https://it.stonybrook.edu/services/zoom )
  • Open the Zoom app
  • Create a new Zoom meeting with only yourself (make sure audio and video are turned on)
  • Share your screen
  • Open your paper presentation in PPT and put in presentation mode
  • Start recording and give a short test presentation to make sure that everything is working smoothly (use mouse as necessary to highlight specific regions of your slides)
  • Stop recording and quit the meeting
  • Open the newly created video (using QuickTime or some other video player) to make sure that your test presentation has both audio and video and looks good
  • Follow the above steps to create your "full-length" video presentation (videos should not exceed 20-25 minutes)
  • Email your video to ALL Instructors who will make it available to the class (please name your Zoom video Lastname_Paper1.mp4 or Lastname_Paper2.mp4 )

Oral Presentation Guidelines: Pre-recorded talks should be formal (as if at a scientific meeting or job talk), presented in PPT format, and be 20-25 minutes long. All talks will be posted on the course website. References should occur at the bottom of each slide when necessary. Presentations should be based mostly on the primary references however secondary references and other sources may be required to make some presentations complete. It is the responsibility of each presenter to email their talk by Friday at 5PM before the week in which their talk is being discussed. Talks will likely be arranged in the following order:

  • Introduction/Background (include biological relevance if applicable)
  • Specifics of the System or General Problem
  • Computational Methods (theory) and Details (system setup) being used
  • Results and Discussion (critical interpretation of results and any problems/challenges)
  • Conclusions/Future
  • Acknowledgments

Speaker and Presentation
Primary Reference
Secondary Reference
2020.08.24 Mon
  • Organizational Meeting
Rizzo, R. mp4 Course introduction and format. Go over Syllabus. Course participant background and introductions.
2020.08.26 Wed


  • Drug Discovery
1. Introduction, history, irrational vs. rational
2. Viral Target Examples

Rizzo, R. mp4
Rizzo, R. pdf

1. Jorgensen, W.L., The many roles of computation in drug discovery. Science 2004, 303, 1813-8

2. Kuntz, I. D., Structure-based strategies for drug design and discovery. Science 1992, 257, 1078-1082

2020.08.31 Mon
  • Chemistry Review
1. Molecular structure, bonding, graphical representations
2. Functionality, properties of organic molecules

Rizzo, R. mp4

2020.09.02 Wed
  • Biomolecular Structure
1. Lipids, carbohydrates
2. Nucleic acids, proteins

Rizzo, R. mp4

structures of the 20 amino acid side chains
2020.09.07 Mon
  • No Class: Labor Day
2020.09.09 Wed
  • Molecular Interactions and Recognition
1. Electrostatics, VDW interactions, hydrophobic effect, molecular recognition (binding energy)
2. Inhibitors types: allosteric, transition state, covalent vs non-covalent, selective, competitive
Rizzo, R. mp4
2020.09.14 Mon
  • Intro. to Methods in 3-D Structure Determination
1. Crystallography, NMR
2. Structure Quality, PDB in detail
Rizzo, R. mp4
Take home QUIZ for Section 1 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
2020.09.16 Wed


  • Classical Force Fields
1. All-atom Molecular Mechanics

1. Adams, Dexter mp4

2. Chang, Jamie mp4

1. Mackerell, A. D., Jr., Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 2004, 25, 1584-604

2. Jorgensen, W. L.; et al., Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids. J. Am. Chem. Soc. 1996, 118, 11225-11236

1. van Gunsteren, W. F.; et al., Biomolecular modeling: Goals, problems, perspectives. Angew. Chem. Int. Ed. Engl. 2006, 45, 4064-92

2. Jorgensen, W. L.; et al., The Opls Potential Functions For Proteins - Energy Minimizations For Crystals of Cyclic-Peptides and Crambin. J. Am. Chem. Soc. 1988, 110, 1657-1671

2020.09.21 Mon
  • Classical Force Fields
  • Explicit Solvent Models
2. Water models (TIP3P, TIP4P, SPC)

1. Corbo, Chris mp4

2. Chung, So Young mp4

1. Cornell, W. D.; et al., A Second Generation Force Field For the Simulation of Proteins, Nucleic Acids, and Organic Molecules. J. Am. Chem. Soc. 1995, 117, 5179-5197

2. Jorgensen, W. L.; et al., Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926-935

1. Bayly, C. I.; et al., A Well-Behaved Electrostatic Potential Based Method Using Charge Restraints For Deriving Atomic Charges - the RESP Model. J. Phys. Chem. 1993, 97, 10269-10280

2020.09.23 Wed
  • Explicit Solvent Models
1. Condensed-phase calculations (DGhydration)
  • Continuum Solvent Models
2. Generalized Born Surface Area (GBSA)

1. Dovedytis, Matt mp4

2. Foran, Chris mp4

1. Jorgensen, W. L.; et al., Monte Carlo Simulation of Differences in Free Energies of Hydration. J. Chem. Phys. 1985, 83, 3050-3054

2. Still, W. C.; et al., Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. J. Am. Chem. Soc 1990, 112, 6127-6129

2020.09.28 Mon
  • Continuum Solvent Models
1. Poisson-Boltzmann Surface Area (PBSA)
2. Accuracy of partial atomic changes for GBSA and PBSA

1. Hall, Carole mp4

2. Lang, Liam mp4

1. Sitkoff, D.; et al., Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models. J. Phys. Chem. 1994, 98, 1978-1988

2. Rizzo, R. C.; et al., Estimation of Absolute Free Energies of Hydration Using Continuum Methods: Accuracy of Partial Charge Models and Optimization of Nonpolar Contributions. J. Chem. Theory. Comput. 2006, 2, 128-139

Take home QUIZ for Section 2 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
2020.09.30 Wed


  • Molecular Conformations
1. Small molecules, peptides, relative energy, minimization methods
  • Sampling Methods for Large Simulations
2. Molecular dynamics (MD)

1. Mingione, Victoria mp4

2. Palmeri, Chris mp4

1. Howard, A. E.; Kollman, P. A., An analysis of current methodologies for conformational searching of complex molecules. J. Med. Chem. 1988, 31, 1669-75

2. Karplus, M.; Petsko, G. A., Molecular dynamics simulations in biology. Nature 1990, 347, 631-9

1. Section 4 (PAGES 22-27) Colby College Molecular Mechanics Tutorial Introduction, 2004, Shattuck, T.W., Colby College

1. Holloway, M. K., A priori prediction of ligand affinity by energy minimization. Perspect. Drug Discov. Design 1998, 9-11, 63-84

2020.10.05 Mon
  • Sampling Methods for Large Simulations
1. Monte Carlo (MC)
  • Predicting Protein Structure
2. Ab initio structure prediction (protein-folding)

1. Pasumarthy, Sishir mp4

2. Quispe-Carbajal, Mariella mp4

1. Metropolis Monte Carlo Simulation Tutorial, LearningFromTheWeb.net, Accessed Oct 2008, Luke, B.

1. Jorgensen, W. L.; TiradoRives, J., Monte Carlo vs Molecular Dynamics for Conformational Sampling. J. Phys. Chem. 1996, 100,14508-14513

2. Dill, K. A.; Chan, H. S., From Levinthal to pathways to funnels. Nat. Struct. Biol. 1997, 4, 10-19

1. Metropolis, N.;et al., Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21, 1087-1092

2020.10.07 Wed
  • Predicting Protein Structure
1. Example Trp-cage
2. Comparative (homology) modeling

1. Rajesh, Chandana mp4

2. Rangwala, Aziz mp4

1. Simmerling, C.;et al., All-atom structure prediction and folding simulations of a stable protein. J. Am. Chem. Soc. 2002, 124,11258-9

2. Marti-Renom, M. A.; et al., Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 2000,29,291-325

1. Daggett, V.; Fersht, A., The present view of the mechanism of protein folding. Nat. Rev. Mol. Cell Biol. 2003, 4, 497-502

2. Fiser, A.; et al., Evolution and physics in comparative protein structure modeling. Acc. Chem. Res. 2002, 35, 413-21

2020.10.12 Mon
  • Predicting Protein Structure
1. Case studies (CASP)
2. Accelerated MD for Blind Protein Prediction

1. Sadatrezaei, Golbahar mp4

2. Steier, Joshua mp4

1. Moult, J., A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 2005,15, 285-9

2. Perez, A.; et al., Blind protein structure prediction using accelerated free-energy simulations. Sci. Adv. 2016, 2

1. Kryshtafovych, A.; et al., Progress over the first decade of CASP experiments. Proteins 2005, 61 Suppl 7, 225-36

2020.10.14 Wed
  • Predicting Protein Structure
1. MD x-ray refinement
2. Protein Design

1. Contorno, Shaymus mp4

2. He, Yongle mp4

1. Brunger, A. T.;Adams, P. D., Molecular dynamics applied to X-ray structure refinement. Acc. Chem. Res. 2002, 35, 404-12

2. Kuhlman, A. T.;et al, Design of a Novel Globular Protein Fold with Atomic-Level Accuracy. Science 2003, 302, 1364-1368

1. Adams, P. D.; et al., Extending the limits of molecular replacement through combined simulated annealing and maximum-likelihood refinement. Acta Crystallogr D Biol Crystallogr 1999, 55, 181-90

Take home QUIZ for Section 3 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
2020.10.19 Mon


  • Docking
1. Introduction to DOCK
2. Test Sets (pose reproduction)

1. Hetherington, Caitlin mp4

2. Koller, Angus mp4

1. Moustakas, D. T.; et al., Development and Validation of a Modular, Extensible Docking program: DOCK 5. J. Comput. Aided Mol. Des. 2006, 20, 601-619

2. Mukherjee, S.; et al., Docking Validation Resources: Protein Family and Ligand Flexibility Experiments. J. Chem. Info. Model. 2010, 50, 1986-2000

1. Ewing, T. J.; et al., DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des. 2001, 15, 411-28

2. The CCDC/Astex Test Set

2020.10.21 Wed
  • Docking
1. Test Sets (virtual screening)
2. Test Sets (database enrichment)

1. Pak, Steven mp4

2. Adams, Dexter mp4

1. Irwin, J. J.; Shoichet, B. K., ZINC--a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 2005, 45, 177-82

2. Huang, N.; et al., Benchmarking Sets for Molecular Docking. J. Med. Chem. 2006, 49(23), 6789-6801

1. ZINC Website at UCSF, Shoichet group

2020.10.26 Mon
  • Docking
1. Footprint-based scoring
  • Discovery Methods
2. Hotspot probes (GRID)

1. Chang, Jaime mp4

2. Corbo, Chris mp4

1. Balius, T.E.; et al., Implementation and Evaluation of a Docking-Rescoring Method Using Molecular Footprint Comparisons. J. Comput. Chem. 2011, 32, 2273-2289.

2. Goodford, P. J., A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-57

2020.10.28 Wed
  • Discovery Methods
2 Pharmacophores

1. Chung, So Young mp4

2. Dovedytis, Matt mp4

1. Kubinyi, H., Comparative molecular field analysis (CoMFA). Encyclopedia of Computational Chemistry, Databases and Expert Systems Section, John Wiley & Sons, Ltd. 1998

2. Chang, C.; et al., Pharmacophore-based discovery of ligands for drug transporters. Advanced Drug Delivery Reviews 2006, 58, 1431-1450

1. Cramer, R. D.; Patterson, D. E.; Bunce, J. D., Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc., 1988, 110, 5959-5967

2020.11.02 Mon
  • Discovery Methods.
1. Pharmacophores
2. De novo design

1. Foran, Chris mp4

2. Hall, Carole mp4

1. Alvarez, J.; et al., Pharmacophore-Based Molecular Docking to Account for Ligand Flexibility. Proteins 2003, 51, 172-188

2. Cheron, N.; et al., OpenGrowth: An Automated and Rational Algorithm for Finding New Protein Ligands. J. Med. Chem. 2016, 59, 4171-4188

2020.11.04 Wed
  • Discovery Methods
1. De novo design
2. Genetic Algorithm

1. Lang, Liam mp4

2. Mingione, Victoria mp4

1. Jorgensen, W.; et al., Efficient drug lead discovery and optimization. Acc. of Chem. Research 2009, 42 (6), 724-733

2. Pegg, S. C.; Haresco, J. J.; Kuntz, I. D., A genetic algorithm for structure-based de novo design. J Comput Aided Mol Des 2001, 15, 911-33

Take home QUIZ for Section 4 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
2020.11.09 Mon


  • Free Energy Methods
1. Thermolysin with two ligands (FEP)
2. Fatty acid synthase I ligands (TI)

1. Palmeri, Chris mp4

2. Pasumarthy, Sishir mp4

1. Bash, P. A.; Singh, U. C.; Brown, F. K.; Langridge, R.; Kollman, P. A., Calculation of the relative change in binding free energy of a protein-inhibitor complex. Science 1987, 235, 574-6

2. Labahn, A.; et al., Free energy calculations on the binding of novel thiolactomycin derivatives to E. coli fatty acid synthase I. Bioorg Med Chem. 2012, 20, 3446-53

1&2. Jorgensen, W. L., Free Energy Calculations: A Breakthrough for Modeling Organic Chemistry in Solution. Accounts Chem. Res. 1989, 22, 184-189

1&2. Kollman, P., Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93, 2395-2417

2. Lawrenz, M.; et al., Independent-Trajectories Thermodynamic-Integration Free-Energy Changes for Biomolecular Systems: Determinants of H5N1 Avian Influenza Virus Neuraminidase Inhibition by Peramivir. J. Chem. Theory Comput. 2009, 5, 1106-1116

2020.11.11 Wed
1. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods
  • MM-GBSA case studies
2. EGFR and mutants

1. Quispe-Carbajal, Mariella mp4

2. Rajesh, Chandana mp4

1. Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S. H.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D. A.; Cheatham, T. E., Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Accounts Chem. Res. 2000, 33, 889-897

2. Balius, T.E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry, 2009, 48, 8435-8448

2020.11.16 Mon
  • MM-GBSA case studies
1. ErbB family selectivity
  • Linear Response
2. Intro to Linear Response (LR method)

1. Rangwala, Aziz mp4

2. Sadatrezaei, Golbahar mp4

1. Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406

2. Aqvist, J.; Mowbray, S. L., Sugar recognition by a glucose/galactose receptor. Evaluation of binding energetics from molecular dynamics simulations. J Biol Chem 1995, 270, 9978-81

2020.11.18 Wed
  • Linear Response
1. Inhibition of protein kinases (Extended LR method)
  • Properties of Known Drugs
2. Molecular Scaffolds (frameworks) and functionality (side-chains

1. Steier, Joshua mp4

2. Contorno, Shaymus mp4

1. Tominaga, Y.; Jorgensen, W. L.; General model for estimation of the inhibition of protein kinases using Monte Carlo simulations. J. Med. Chem. 2004, 47, 2534-2549

2. Bemis, G. W.; Murcko, M. A., The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 1996, 39, 2887-93

2. Bemis, G. W.; Murcko, M. A., Properties of known drugs. 2. Side chains. J. Med. Chem. 1999, 42, 5095-9

2020.11.23 Mon
  • No Class: Thanksgiving
2020.11.25 Wed
  • No Class: Thanksgiving
2020.11.30 Mon
  • Properties of Known Drugs
1. Lipinski Rule of Five
2 ADME Prediction

1. He, Yongle mp4

2. Hetherington, Caitlin mp4

1. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J., Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Deliv. Rev. 2001, 46, 3-26

2. Hou, T. J.; Xu, X. J.; ADME evaluation in drug discovery. J. Mol. Model, 2002, 8, 337-349

1. Lipinski, C. A., Chris Lipinski discusses life and chemistry after the Rule of Five. Drug. Discov. Today 2003, 8, 12-6

2. Hou, T. J.; Xu, X. J.; AMDE Evaluation in drug discovery 3. Modeling blood-brain barrier partitioning using simple molecular descriptors. J. Chem. Inf. Comput. Sci., 2003, 43, 2137-2152

2020.12.02 Wed
  • TBD

1. Koller, Angus mp4

2. Pak, Steve mp4

2020.12.07 Mon
  • TBD
Take home QUIZ for Section 5 starts after today's class (4:00PM) and must be emailed to all Instructors within 24 hours (4:00PM tomorrow)
  • Final Exam
No Final Exam in AMS-535/CHE-535 for Fall 2020

Required Syllabi Statements:

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Academic Integrity Statement: 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 http://www.stonybrook.edu/commcms/academic_integrity/index.html

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