2022 AMS-535 Fall

Please see https://ringo.ams.stonybrook.edu/~rizzo for Rizzo Group Homepage

Instructor	Dr. Robert C. Rizzo [631-632-8519, rizzorc -at- gmail.com] Brock Boysan [631-632-8519, brock dot boysan -at- stonybrook dot edu]
Course No.	AMS-535 / CHE-535
Location/Time	ESS 069, 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 1-2 ZOOM presentations based on papers assigned from the schedule below which will be uploaded to the class wiki for viewing by class participants (2) Class discussion (30%) At scheduled class times, students will attend class in person and asked to discuss the papers they have read and the presentations they have watched. For each paper, each student will prepare 2 thoughtful questions ahead of time to facilitate scientific discussion. The two questions for each paper, along with any discussion notes (if the question was discussed), will be uploaded to Google Forms by midnight of each day's class (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 oral presentations, class discussion/attendance/participation, and quizzes.

Learning Objectives

• (1) Become informed about the field of computational structure-based drug design and the pros and cons of its methods.
• (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.

Syllabus Notes

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) and class will be held in person. There is no online section.
• 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.
• 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 which are held in person the Instructors(s) will ask participants to explain details of the papers they have read which will form the basis of the "Discussion/Participation" part of their grade. Thus, it is important that everyone attend all of the classes. All students are expected to participate especially those whose papers are being discussed that day. As noted above each student will prepare two questions per paper to facilitate discussion. Your questions, and any answers that may have been discussed in class, will be uploaded into Google Forms (details to be given in class) by midnight of the day the papers are covered.
• If a student is unable to attend a specific class for reasons beyond their control they will instead be asked to email the instructor(s) AND 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 a class they will have 24 hours to submit their Paper Summary Sheets. Late Paper Summary Sheets will not be accepted.
• Please note that this is an in-person class and all participants are expected to attend each and every class.

Oral Presentations:

• Students will pre-record 1-2 ZOOM presentations based on papers assigned to them 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 complete 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

-

Date	Topic	Speaker and Presentation	Primary Reference	Secondary Reference
2022.08.22 Mon	Organizational Meeting	Rizzo, R. mp4	Course introduction and format. Go over Syllabus. Course participant background and introductions.	-
2022.08.24 Wed	SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE 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	-
2022.08.29 Mon	Chemistry Review 1. Molecular structure, bonding, graphical representations 2. Functionality, properties of organic molecules	Rizzo, R. mp4 Rizzo, R. pdf	in class lecture	-
2022.08.31 Wed	Biomolecular Structure 1. Lipids, carbohydrates 2. Nucleic acids, proteins	Rizzo, R. mp4 Rizzo, R. pdf	in class lecture	structures of the 20 amino acid side chains
2022.09.05 Mon	No Class: Labor Day	-	-	-
2022.09.07 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 Rizzo, R. pdf	in class lecture	-
2022.09.12 Mon	Intro. to Methods in 3-D Structure Determination 1. Crystallography, NMR 2. Structure Quality, PDB in detail	Rizzo, R. mp4 Rizzo, R. pdf	in class lecture	-
-	-	-	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)	-
2022.09.14 Wed	SECTION II: MOLECULAR MODELING Classical Force Fields 1. All-atom Molecular Mechanics 2. OPLS	1. Outhwaite, Ian mp4 pdf 2. Marr, Douglas mp4 pdf	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
2022.09.19 Mon	Classical Force Fields 1. AMBER Explicit Solvent Models 2. Water models (TIP3P, TIP4P, SPC)	1. Li, Stan mp4 pdf 2. Dar, Nauroz mp4 pdf	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
2022.09.21 Wed	Explicit Solvent Models 1. Condensed-phase calculations (DGhydration) Continuum Solvent Models 2. Generalized Born Surface Area (GBSA)	1. Mannaf, Aniqa mp4 pdf 2. Biju, Bismi mp4 pdf	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	1. Thermodynamic Cycle as Drawn By Dr. Rizzo -
2022.09.26 Mon	Continuum Solvent Models 1. Poisson-Boltzmann Surface Area (PBSA) 2. Accuracy of partial atomic changes for GBSA and PBSA	1. Kakade, Yogesh mp4 pdf 2. Becker, Kindra mp4 pdf	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)	Additional resources: 1. Duarte Ramos Matos, G.; et al., Approaches for Calculating Solvation Free Energies and Enthalpies Demonstrated with an Update of the FreeSolv Database. J. Chem. Eng. Data 2017, 62, 1559-1569 2. Loeffler, H. H.; et al., Reproducibility of Free Energy Calculations across Different Molecular Simulation Software Packages J. Chem. Theory Comput. 2018, 14, 5567−5582
2022.09.28 Wed	SECTION III: SAMPLING METHODS Molecular Conformations 1. Small molecules, peptides, relative energy, minimization methods Sampling Methods for Large Simulations 2. Molecular dynamics (MD)	1. Snyders, Hayden mp4 pdf 2. Cheung, Andrew mp4 pdf	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
2022.10.03 Mon	Sampling Methods for Large Simulations 1. Monte Carlo (MC) Predicting Protein Structure 2. Ab initio structure prediction (protein-folding)	1. Cordero, Ricardo mp4 pdf 2. Chong, Jiyun mp4 pdf	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
2022.10.05 Wed	Predicting Protein Structure 1. Example Trp-cage 2. Comparative (homology) modeling	1. Ventura, Carlos mp4 pdf 2. Dharmajeewa, Oshini mp4 pdf	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
2022.10.10 Mon	No Class: Fall Break	-	-	-
2022.10.12 Wed	Predicting Protein Structure 1. Case studies (CASP) 2. Accelerated MD for Blind Protein Prediction	1. No online presentation for paper #1 2. Moy, Olivia mp4 pdf	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
2022.10.17 Mon	Predicting Protein Structure 1. Meso-Scale MD (Markov State Models) 2. Neural Networks	1. Henderson, Max mp4 pdf 2. Bauer, Jack mp4 pdf	1. Durrant, J.;et al, Mesoscale All-Atom Influenza Virus Simulations Suggest New Substrate Binding Mechanism. ACS Central Science 2020, 6, 189-196 2. Senior, A.;et al, Improved protein structure prediction using potentials from deep learning. Nature 2020, 577, 1-5	1. Husic, B.; et al., Markov State Models: From an Art to a Science. JACS 2018, 140, 2386-2396
-	-	-	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)	-
2022.10.19 Wed	SECTION IV: LEAD DISCOVERY Docking 1. Introduction to DOCK 2. Test Sets (database enrichment)	1. Kaur, Gurlin mp4 pdf 2. Sayed, Aaliya mp4 pdf	1. Allen, W. J.; et al., DOCK 6: Impact of New Features and Current Docking Performance. Journal of computational chemistry 2015, 36, 1132-1156. 2. Mysinger, M.; et al., Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. Journal of medicinal chemistry 2012, 55, 6582-94	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
2022.10.24 Mon	Docking 1. Test Sets (virtual screening) 2. Footprint-based scoring	1. Urena, Jailene mp4 pdf 2. Boysan, Brock mp4 pdf	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. Balius, T.E.; et al., Implementation and Evaluation of a Docking-Rescoring Method Using Molecular Footprint Comparisons. J. Comput. Chem. 2011, 32, 2273-2289.	1. ZINC Website at UCSF, Shoichet group
2022.10.26 Wed	Docking 1. GIST score Discovery Methods 2. Hotspot probes (GRID)	1. Outhwaite, Ian mp4 pdf 2. Marr, Douglas mp4 pdf	1. Balius, T.E.; et al., Testing inhomogeneous solvation theory in structure-based ligand discovery. Proceedings of the National Academy of Sciences 2017, 114, 201703287. 2. Goodford, P. J., A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 1985, 28, 849-57	1. Nguyen, C.; et al., Thermodynamics of Water in an Enzyme Active Site: Grid-Based Hydration Analysis of Coagulation Factor Xa. Journal of chemical theory and computation 2014, 10, 2769-2780.
2022.10.31 Mon	Discovery Methods 1. COMFA 2 Pharmacophores	1. Li, Stan mp4 pdf 2. Dar, Nauroz mp4 pdf	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
2022.11.02 Wed	Discovery Methods. 1. Pharmacophores 2. De novo design	1. Mannaf, Aniqa mp4 pdf 2. Biju, Bismi mp4 pdf	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	-
2022.11.07 Mon	Discovery Methods 1. De novo design 2. Genetic Algorithm	1. Kakade, Yogesh mp4 pdf 2. Becker, Kindra mp4 pdf	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)	-
2022.11.09 Wed	SECTION V: LEAD REFINEMENT Free Energy Methods 1. Thermolysin with two ligands (FEP) 2. Fatty acid synthase I ligands (TI)	1. Snyders, Hayden mp4 pdf 2. Cheung, Andrew mp4 pdf	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
2022.11.14 Mon	MM-PB/GBSA 1. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods MM-GBSA case studies 2. EGFR and mutants	1. Cordero, Ricardo mp4 pdf 2. Chong, Jiyun mp4 pdf	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	1. Bartlett, P. A.; Marlowe, C. K., Evaluation of Intrinsic Binding Energy from a Hydrogen Bonding Group in an Enzyme Inhibitor. Science. 1987, 235, 569-571
2022.11.16 Wed	MM-GBSA case studies 1. ErbB family selectivity Linear Response 2. Intro to Linear Response (LR method)	1. Ventura, Carlos mp4 pdf 2. Dharmajeewa, Oshini mp4 pdf	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	-
2022.11.21 Mon	Linear Response 1. Inhibition of protein kinases (Extended LR method) Properties of Known Drugs 2. Molecular Scaffolds (frameworks) and functionality (side-chains	1. No online presentation for paper #1 2. Moy, Olivia mp4 pdf	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	-
2021.11.23 Wed	No Class: Thanksgiving	-	-	-
2022.11.28 Mon	Properties of Known Drugs 1. Lipinski Rule of Five 2 ADME Prediction	1. Henderson, Max mp4 pdf 2. Bauer, Jack mp4 pdf	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
2022.11.30 Wed	Properties of Known Drugs 1. Synthetic Accessibility 2. QED	1. Kaur, Gurlin mp4 pdf 2. Sayed, Aaliya mp4 pdf	1. Ertl, P.; Schuffenhauer, A.; Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminformatics, 2009, 1, 8 2. Bickerton, G. R., Quantifying the chemical beauty of drugs. Nature Chemistry 2012, 4, 90-98	-
2022.12.05 Mon	From Target to Market 1. Drug Discovery Overview 2. Attrition Rates	1. Urena, Jailene mp4 pdf 2. No online presentation for paper #2	1. Hughes, J.; et. al., Principles of early drug discovery. British journal of pharmacology 2010, 162, 1239-49. 2. Waring, M.; et. al., An analysis of the attrition of drug candidates from four major pharmaceutical companies Nature reviews 2015, 14.	2. Understanding Clinical Trial Terminology: What's a Phase 1, 2 OR 3 Clinical Trial?. www.concertpharma.com, 2019 Thermodynamic Cycles Course Wrap-Up Topics	-	-
-	-	-	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 2022	-

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

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 Student Conduct and Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. Until/unless the latest COVID guidance is explicitly amended by SBU, during Fall 2021"disruptive behavior” will include refusal to wear a mask during classes. For the latest COVID guidance, please refer to: https://www.stonybrook.edu/commcms/strongertogether/latest.php