Difference between revisions of "2023 AMS-535 Fall"

Revision as of 13:02, 18 September 2023

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

Instructors	Dr. Robert C. Rizzo [631-632-8519, rizzorc -at- gmail.com] Steven Pak [631-632-8519, steven dot pak -at- stonybrook dot edu] John Bickel [631-632-8519, john dot bickel -at- stonybrook dot edu]
Course No.	AMS-535 / CHE-535
Location/Time	Mathematics S235S, Monday and Wednesday 2:30PM - 3:50PM
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 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. Copies of these questions will be handed to the instructors prior to the beginning of discussion. (3) In-class quizzes (45%) Five in class 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 2:30-3:50PM) 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 in-person discussion, in breakout groups, facilitated by an assigned discussion leader. Papers will be read prior to meeting (1-2 per class) for which everyone will also have watched an oral presentation on the paper (1-2 per class). Oral presentations will be in the form of pre-recorded videos made by students taking the class.
For each paper discussed, one (1) student in each discussion group will be assigned as discussion leader. The discussion group leader will be expected to understand and guide discussion on the paper. If there is any confusion regarding paper content as a discussion leader, students should reach out to TAs well before the class in which they are to lead.
The Instructors(s) will moderate and participate in discussion, as well as field questions and clear up any misunderstandings, and take over discussion when necessary.
For each paper, each student will prepare 2 thoughtful questions ahead of time to facilitate scientific discussion. Copies of these questions will be handed to the instructors prior to the beginning of discussion.
A sizeable portion of the class grade (30%) is based on these discussion sections. Thus, it is important that everyone attend all of the classes and participate in each discussion to receive full credit.
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 student misses a 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 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 and, independently, read the paper 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 was discussed.

In-class Quizzes:

Five quizzes will be used to assess student understanding of the course material.
The quiz format is in-class, closed book.
Answers to quiz questions should integrate topics, concepts, and outcomes of the different papers covered for the section being tested.
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.

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

Class Schedule

-

Date	Topic	Speaker and Presentation	Primary Reference	Secondary Reference
2023.08.28 Mon	Organizational Meeting	Rizzo, R. mp4	Course introduction and format. Go over Syllabus. Course participant background and introductions.	-
2023.08.30 Wed	SECTION I: DRUG DISCOVERY AND BIOMOLECULAR STRUCTURE Drug Discovery Introduction, history, irrational vs. rational Viral Target Examples	Rizzo, R. pdf pdf_handout	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	-
2023.09.04 Mon	No Class: Labor Day	-	-	-
2023.09.06 Wed	Chemistry Review Molecular structure, bonding, graphical representations Functionality, properties of organic molecules	Rizzo, R. pdf	in class lecture	-
2023.09.11 Mon	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
2023.09.13 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	-
2023.09.18 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	-
-	-	-	IN CLASS QUIZ for Section 1 2023.09.20 WED	-
2023.09.20 Wed	QUIZ #1 FIRST 30 MINUTES SECTION II: MOLECULAR MODELING Classical Force Fields 1. All-atom Molecular Mechanics	1. Alseika, Zachary mp4 mov pdf	1. Mackerell, A. D., Jr., Empirical force fields for biological macromolecules: overview and issues. J. Comput. Chem. 2004, 25, 1584-604	1. van Gunsteren, W. F.; et al., Biomolecular modeling: Goals, problems, perspectives. Angew. Chem. Int. Ed. Engl. 2006, 45, 4064-92
2023.09.25 Mon	Classical Force Fields 1. OPLS 2. AMBER	1. Pina, Liliana mp4 pdf 2. Bickel, John mp4 pdf	1. 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 2. 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	1. 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 2. 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
2023.09.27 Wed	Explicit Solvent Models 1. Water models (TIP3P, TIP4P, SPC) 2. Condensed-phase calculations (DGhydration)	1. Boysan, Brock mp4 pdf 2. Dwulit, Catherine mp4 pdf	1. Jorgensen, W. L.; et al., Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926-935 2. Jorgensen, W. L.; et al., Monte Carlo Simulation of Differences in Free Energies of Hydration. J. Chem. Phys. 1985, 83, 3050-3054	2. Thermodynamic Cycle as Drawn By Dr. Rizzo
2023.10.02 Mon	Continuum Solvent Models 1. Generalized Born Surface Area (GBSA) 2. Poisson-Boltzmann Surface Area (PBSA)	1. Bushati, Aldo mp4 pdf 2. Dreher, Kathleen mp4 pdf	1. Still, W. C.; et al., Semianalytical Treatment of Solvation for Molecular Mechanics and Dynamics. J. Am. Chem. Soc 1990, 112, 6127-6129 2. Sitkoff, D.; et al., Accurate Calculation of Hydration Free Energies Using Macroscopic Solvent Models. J. Phys. Chem. 1994, 98, 1978-1988	-
-	-	-	IN CLASS QUIZ for Section 2 2023.10.04 WED	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
2023.10.04 Wed	SECTION III: SAMPLING METHODS Molecular Conformations 1. Small molecules, peptides, relative energy, minimization methods	1. Kim, Joseph 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	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
2023.10.09 Mon	No Class: Fall Break	-	-	-
2023.10.11 Wed	Sampling Methods for Large Simulations 1. Molecular dynamics (MD) 2. Monte Carlo (MC)	1. Zhu, Zeru mp4 pdf 2. Wodzenski, Nicholas mp4 pdf	1. Karplus, M.; Petsko, G. A., Molecular dynamics simulations in biology. Nature 1990, 347, 631-9 2. Metropolis Monte Carlo Simulation Tutorial, LearningFromTheWeb.net, Accessed Oct 2008, Luke, B. 2. Jorgensen, W. L.; TiradoRives, J., Monte Carlo vs Molecular Dynamics for Conformational Sampling. J. Phys. Chem. 1996, 100,14508-14513	2. Metropolis, N.;et al., Equation of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21, 1087-1092
2023.10.16 Mon	Predicting Protein Structure 1. Ab initio structure prediction (protein-folding) Predicting Protein Structure 2. Example Trp-cage	1. Corbo, Chris mp4 pdf 2. Dharan, Aishwarya mp4 pdf	1. Dill, K. A.; Chan, H. S., From Levinthal to pathways to funnels. Nat. Struct. Biol. 1997, 4, 10-19 2. Simmerling, C.;et al., All-atom structure prediction and folding simulations of a stable protein. J. Am. Chem. Soc. 2002, 124,11258-9	2. 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
2023.10.18 Wed	Predicting Protein Structure 1. Comparative (homology) modeling 2. Neural Network	1. Kang, Sung Jin mp4 pdf 2. Glukhov, Ernest mp4 pdf	1. Marti-Renom, M. A.; et al., Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 2000,29,291-325 2. Senior, A.;et al, Improved protein structure prediction using potentials from deep learning. Nature 2020, 577, 1-5
2023.10.23 Mon	Predicting Protein Structure 1. Accelerated MD for Blind Protein Prediction 2. Meso-Scale MD (Markov State Models)	1. Aywa, Khalayi mp4 pdf 2. Bickel, John mp4 pdf	1. Perez, A.; et al., Blind protein structure prediction using accelerated free-energy simulations. Sci. Adv. 2016, 2 2. Durrant, J.;et al, Mesoscale All-Atom Influenza Virus Simulations Suggest New Substrate Binding Mechanism. ACS Central Science 2020, 6, 189-196	2. Husic, B.; et al., Markov State Models: From an Art to a Science. JACS 2018, 140, 2386-2396
-	-	-	IN CLASS QUIZ for Section 3 2023.10.25 WED	-
2023.10.25 Wed	SECTION IV: LEAD DISCOVERY Docking 1. Introduction to DOCK	1. Corbo, Chris 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.	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
2023.10.30 Mon	*Docking 1. Test Sets (database enrichment) 2. Test Sets (virtual screening)	1. Dreher, Kathleen mp4 pdf 2. Bushati, Aldo mp4 pdf]	1. 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 2. 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. ZINC Website at UCSF, Shoichet group
2023.11.01 Wed	Docking 1. Footprint-based scoring Discovery Methods 2. Hotspot probes (GRID)	1. Nguyen, An Phuc mp4 pdf 2. Boysan, Brock mp4 pdf	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
2023.11.06 Mon	Discovery Methods 1. COMFA 2 Pharmacophores	1. Alseika, Zachary mp4 pdf 2. Dwulit, Catherine 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
2023.11.08 Wed	Discovery Methods. 1. Pharmacophores 2. De novo design	1. Zhu, Zeru mp4 pdf 2. Corbo, Chris 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	-
2023.11.13 Mon	Discovery Methods 1. De novo design 2. Genetic Algorithm	1. Pak, Steven mp4 pdf 2. Bickel, John 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	-
-	-	-	IN CLASS QUIZ for Section 4 2023.11.15 WED	-
2023.11.15 Wed	SECTION V: LEAD REFINEMENT Free Energy Methods 1. Thermolysin with two ligands (FEP)	1. Kang, Sung Jin 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	1. Tronrud, D.E.; Holden, H.M.; Matthews, B.W.; Structures of Two Thermolysin-Inhibitor Complexes That Differ by a Single Hydrogen Bond. Science 1987, 235, 571-573 1. Jorgensen, W. L., Free Energy Calculations: A Breakthrough for Modeling Organic Chemistry in Solution. Accounts Chem. Res. 1989, 22, 184-189 1. Kollman, P., Free Energy Calculations: Applications to Chemical and Biochemical Phenomena. Chem. Rev. 1993, 93, 2395-2417
2023.11.20 Mon	Free Energy Methods 1. Fatty acid synthase I ligands (TI) MM-PB/GBSA 2. Intro to Molecular Mechanics Poisson-Boltzmann / Generalized Born Surface Area Methods	1. Dharan, Aishwarya mp4 pdf 2. Pina, Liliana mp4 pdf	1. 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 2. 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	1. See above papers 1. 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 2. 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
2021.11.22 Wed	No Class: Thanksgiving	-	-	-
2023.11.27 Mon	*MM-GBSA case 1. EGFR and mutantsstudies 2. ErbB family selectivity	1. Rizzo, Robert mp4 pdf 2. Aywa, Khalayi mp4 pdf	1. Balius, T.E.; Rizzo, R. C. Quantitative Prediction of Fold Resistance for Inhibitors of EGFR. Biochemistry, 2009, 48, 8435-8448 2. Huang, Y.; Rizzo, R. C. A Water-based Mechanism of Specificity and Resistance for Lapatinib with ErbB Family Kinases, Biochemistry, 2012, 51, 2390-2406	-
2023.11.29 Wed	Linear Response 1. Intro to Linear Response (LR method) 2. Inhibition of protein kinases (Extended LR method)	1. Wodzenski, Nicholas mp4 pdf 2. Glukhov, Ernest mp4 pdf	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 2. 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	-
2023.12.04 Mon	Properties of Known Drugs 1. Molecular Scaffolds (frameworks) and functionality (side-chains) 2. Lipinski Rule of Five	1. Kim, Joseph mp4 pdf 2. Boysan, Brock mp4 pdf	1. Bemis, G. W.; Murcko, M. A., The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 1996, 39, 2887-93 1. Bemis, G. W.; Murcko, M. A., Properties of known drugs. 2. Side chains. J. Med. Chem. 1999, 42, 5095-9 2. 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. Lipinski, C. A., Chris Lipinski discusses life and chemistry after the Rule of Five. Drug. Discov. Today 2003, 8, 12-6
2023.12.06 Wed	Properties of Known Drugs 1 ADME Prediction 2. QED	1. Nguyen, An Phuc mp4 pdf 2. Pak, Steven mp4 pdf	1. Hou, T. J.; Xu, X. J.; ADME evaluation in drug discovery. J. Mol. Model, 2002, 8, 337-349 2. Matos, DRG; Pak, S; Rizzo R; Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. J. Chem. Inf. Model. 2023	1. 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
-	-	-	IN CLASS QUIZ for Section 5 2023.12.11 MON	-
2023.12.11 Mon	From Target to Market 1. Drug Discovery Overview 2. Attrition Rates	1. Bickel, John mp4 pdf	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
-	Final Exam	-	No Final Exam in AMS-535/CHE-535 for Fall 2023	-

Required Syllabi Statements:

The University Senate Undergraduate and Graduate Councils have authorized that the following required statements appear in all teaching syllabi (graduate and undergraduate courses) on the Stony Brook Campus.. This information is also located on the Provost’s website: https://www.stonybrook.edu/commcms/provost/faculty/handbook/academic_policies/syllabus_statement.php

Student Accessibility Support Center Statement: If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact the Student Accessibility Support Center, 128 ECC Building, (631) 632-6748, or at sasc@stonybrook.edu. They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential. Students who require assistance during emergency evacuation are encouraged to discuss their needs with their professors and the Student Accessibility Support Center. For procedures and information go to the following website: https://ehs.stonybrook.edu/programs/fire-safety/emergency-evacuation/evacuation-guide-people-physical-disabilities and search Fire Safety and Evacuation and Disabilities.

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

@@ Line 551: / Line 551: @@
    | 1. [https://ringo.ams.stonybrook.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/References/Xu001.pdf Hou, T. J.; Xu, X. J.; ADME evaluation in drug discovery. ''J. Mol. Model'', '''2002''', ''8'', 337-349]
-. [https://ringo.ams.stonybrook.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/References/Bickerton2012.pdf Bickerton, G. R., Quantifying the chemical beauty of drugs. ''Nature Chemistry'' '''2012''', ''4'', 90-98]
+. [https://ringo.ams.stonybrook.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/References/matos_pak_2023.pdf Matos, DRG; Pak, S; Rizzo R; Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. ''J. Chem. Inf. Model.'' '''2023''']
    | 1. [https://ringo.ams.stonybrook.edu/~rizzo/StonyBrook/teaching/AMS532_AMS535_AMS536/References/Xu003.pdf 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]
 |- style="background:lightgreen"

Difference between revisions of "2023 AMS-535 Fall"

Revision as of 13:02, 18 September 2023

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

Syllabus Notes

Class Schedule

Required Syllabi Statements:

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