Course Information

Course Name	Turkish	Kemoenformatik
	English	Chemoinformatics
Course Code	HBM 621E	Credit	Lecture (hour/week)	Recitation (hour/week)	Laboratory (hour/week)
Semester	-	3	3	-	-
Course Language		English
Course Coordinator		Adem Tekin
Course Objectives		1. Introducing different chemoinformatics methods 2. Providing examples on the use of chemoinformatics in drug design research 3. Gaining practical experience with Python on the methods used in chemoinformatics
Course Description		The scope and history of chemoinformatics, Representation and manipulation of 2D and 3D molecular structures, Overview of databases/data sources, Calculation of physical and chemical data: molecular mechanics and quantum mechanics, Methods for data analysis: machine learning techniques in chemistry, neural networks, evolutionary algorithms and clustering techniques, Molecular descriptors, Quantitative Structure – Activity Relationship (QSAR), Drug design, Similarity methods, Analysis of high-throughput screening data, Virtual screening and docking, Combinatorial chemistry and library design.
Course Outcomes		M.Sc./Ph.D. students who successfully pass this course gain knowledge, skill and competency in the following subjects; 1. Interpret the most important formats used for describing molecular structures 2. Have a good knowledge of databases used in chemoinformatics 3. Argue on how to choose the appropriate computational tools for a given problem 4. Describe the most widely used data analysis tools in chemoinformatics 5. Have a good working knowledge on QSAR 6. Have an overview of the virtual screening techniques 7. Have an overview of the available computational drug design techniques and their uses 8. Write and Implement algorithms for various chemoinformatics problems
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