|
|
| SEC 311 | Computer Aided Drug Design | 1+0+0 | ECTS:2 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of DENTISTRY | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 1 hour of lectures per week | | Lecturer | Öğr. Gör. Dr Ercüment YILMAZ | | Co-Lecturer | Assoc. Prof. Dr. Gizem Tatar YILMAZ | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Identification of potential drug molecules whose pharmacological activity can be predicted by utilizing predefined structure-activity relationships by computer-aided drug design methods. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Gain general information about computer-aided drug design | 1,6 | 1, | | LO - 2 : | To have theoretical knowledge about "Structure Based Drug Design" which is one of the computer aided drug design methods. | 1,6 | 1, | | LO - 3 : | To have theoretical knowledge about "Ligand Based Drug Design" which is one of the computer aided drug design methods. | 1,6 | 1, | | LO - 4 : | Ligand Based Drug Design: Gains theoretical knowledge about QSAR | 1,6 | 1, | | LO - 5 : | Have theoretical knowledge about de novo drug design | 1,6 | 1, | | LO - 6 : | Learn small chemical molecule libraries (ChEMBL, ZINC, PubChem) and data formats (smiles, mol, pdb) | 1,6 | 1, | | LO - 7 : | Learn in silico ADME analysis and applications (Swiss- ADME) | 1,6 | 1, | | LO - 8 : | Learn to use Molecular Docking simulation and applications (AutoDock Tools, AGFR) | 1,6 | 1, | | LO - 9 : | Using Molecular Docking simulation and applications (AutoDock Tools, AGFR), it simulates a candidate molecule with a target structure (protein) and calculates the binding energy. Determine the best ligand to bind to the target structure among multiple molecules. | 1,6 | 1, | | LO - 10 : | Have general knowledge about machine learning applications in computer-aided drug design. | 1,7 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| It involves computer-aided methods for developing useful therapeutic compounds that are more potent than existing ones, less toxic, and with minimized side effects |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Computer-aided drug design: A overview | | | Week 2 | Computer-aided drug design methods: Structure based drug design | | | Week 3 | Structure based drug design: Molecular docking (search algorith, scoring function) | | | Week 4 | Computer-aided drug design methods: Ligand based drug design | | | Week 5 | Ligand based drug design: Pharmacophore | | | Week 6 | Ligand based drug design:QSAR | | | Week 7 | De novo drug desing | | | Week 8 | Small chemical molecules library (ChEMBL,ZINC,PubChem), data formats (smiles, mol, pdb) -1 | | | Week 9 | Small chemical molecules library (ChEMBL,ZINC,PubChem), data formats (smiles, mol, pdb) - 2 | | | Week 10 | Computer representation, drawing and conformational analysis of small chemical molecules | | | Week 11 | In silico ADME analysis and application (Swiss-ADME) | | | Week 12 | Molecular docking simulation and application (AutoDock Tools, AGFR) - 1 | | | Week 13 | Molecular docking simulation and application (AutoDock Tools, AGFR) - 2 | | | Week 14 | Machine Learning Applications in Computer-Aided Drug Design | | | Week 15 | Arasınav | | | Week 16 | Final | | | |
| 1 | Andrew Leach, "Molecular Modelling : Principles and Applications- Second Edition", Pearson, 2021 | | | |
| 1 | Ruth Huey, Garrett M. Morris, Stefano Forli, "Using AutoDock 4 and AutoDock Vina with AutoDockTools: A Tutorial", The Scripps Research Institute Molecular Graphics Laboratory 10550 N. Torrey Pines Rd. La Jolla, California 92037-1000 USA, 2012 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 15 | 29.12.2023 | 00 | 50 | | End-of-term exam | 16 | 10.01.2024 | | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 1 | 14 | 14 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 16 |
|