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| 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 | Dr. Öğr. Üyesi Ercüment YILMAZ | | Co-Lecturer | Assoc. Prof. Dr. Gizem Tatar YILMAZ | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to teach how to utilize structure-activity relationships using computer-aided drug design (CADD) methods in the discovery of potential drug candidates with predictable pharmacological effects. Based on structure-based and ligand-based approaches, the aim is to identify, analyze and evaluate potential therapeutic molecules by in silico methods. Students will learn to identify research topics by effectively searching the current scientific literature and develop projects with open access databases and software. The course encourages the preparation of projects that will contribute to drug development studies for clinical needs in the field of dentistry. |
| 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 : | Have theoretical knowledge about Ligand Based Drug and de novo drug design | 1,6 | 1, | | LO - 5 : | Learn small chemical molecule libraries (ChEMBL, ZINC, PubChem) and data formats (smiles, mol, pdb) | 1,6 | 1, | | LO - 6 : | Learn in silico ADME analysis and applications (Swiss- ADME) | 1,6 | 1, | | LO - 7 : | Learn to use Molecular Docking simulation and applications (AutoDock Tools, AGFR) | 1,6 | 1, | | LO - 8 : | 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 - 9 : | Have general knowledge about machine learning applications in computer-aided drug design. and dentistry | 1,6 | 1, | | LO - 10 : | Learns effective literature review techniques, learns project management techniques. | 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 | | |
| This course covers computer-aided drug design (CADD) approaches for the development of novel therapeutic agents for treatment in dentistry. Focusing on structure-based and ligand-based drug design methods, molecular docking, de novo drug design, conformation analysis, ADME prediction and molecular modeling techniques are covered. In addition, hands-on analysis is performed through chemical compound databases, data formats and software (AutoDock Tools, AGFR, SwissADME). In the context of clinical problems specific to dentistry, how machine learning techniques can be used in computer-aided drug development processes is examined. The course is complemented by literature review skills and project management techniques. The aim is to provide the theoretical and practical knowledge necessary for the development of new drug candidates that are low toxicity, effective and target specific. |
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| 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 | De novo drug desing | | | Week 6 | Small chemical molecules library (ChEMBL,ZINC,PubChem), data formats (smiles, mol, pdb) | | | Week 7 | Computer representation, drawing and conformational analysis of small chemical molecules | | | Week 8 | In silico ADME analysis and application (Swiss-ADME) | | | Week 9 | Molecular docking simulation and application (AutoDock Tools, AGFR) - 1 | | | Week 10 | Molecular docking simulation and application (AutoDock Tools, AGFR) - 2 | | | Week 11 | Machine Learning Applications in Computer-Aided Drug Design and Dentistry | | | Week 12 | Effective Literature Review | | | Week 13 | Project Management Techniques-1 | | | Week 14 | Project Management Techniques-2 | | | 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 | 15.05.2025 | 1 | 50 | | End-of-term exam | 16 | 19.06.2025 | | 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 |
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