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COMPUTATIONAL DRUG DISCOVERY
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COMPUTATIONAL DRUG DISCOVERY
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Academic year 2023/2024
- Course ID
- BIO0220 Pds GEN
- Teachers
- Giulia Caron
Giuseppe Ermondi - Degree course
- [0101M22] Molecular Biotechnology
- Year
- 2nd year
- Teaching period
- Second semester
- Type
- Elective
- Credits/Recognition
- 4
- Course disciplinary sector (SSD)
- CHIM/08 - pharmaceutical chemistry
- Delivery
- Formal authority
- Language
- English
- Attendance
- Obligatory
- Type of examination
- Oral
- Type of learning unit
- corso
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Sommario del corso
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Course objectives
Providing students with the basic concepts and tools to manage the 3D structure of proteins in the perspective of drug design using Web-based and stand-alone tools
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Results of learning outcomes
KNOWLEDGE AND UNDERSTANDING
Acquisition of theoretical skills concerning the use of advanced in-silico techniques used in the drug discovery process
APPLYING KNOWLEDGE AND UNDERSTANDING
Development of the ability to incorporate common in-silico methods and information provided by various internet sources into daily research work
MAKING JUDGEMENTS
Critical evaluation of the information obtained from the use of in-silico techniques
COMMUNICATION SKILLS
Acquisition of oral and written communication of results as well as the ability to use graphical language
LEARNING SKILLS
Acquisition of autonomous learning capacity and self-assessment of its preparation
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Program
- Visualization of 3D structures
- Retrieving 3D structure from PDB and AlphaFold
- Evaluation of the reliability of the structures
- Dynamics of proteins studied using available web tools
- Building of the 3D structure of mutated proteins and prediction of their effects
- Membranes in drug discovery
- Permeability basic concepts
- 2D molecular descriptors to predict permeability
- PerMM to madel translocation pathways
- The impact of conformational variability on permeability
- 3D molecular descriptors to predict permeability
- Application to cyclic peptides
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Course delivery
The lessons will be given in form of frontal and practical lessons in which students will be asked to test software and webservers discussed during the frontal lessons
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Learning assessment methods
Students are encouraged to participate, ask questions and maintain a direct relationship with the professor during the lesson.
In each lesson, they are involved in practical exercitation based on online or free tools that they can perform on their notebooks.
Finally, for each topic, students must submit a report that will be evaluated
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Support activities
Classroom practice
Suggested readings and bibliography
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Notes
Readings and bibliography will be suggested during the lessons
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