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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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Last update: 12/05/2023 18:40
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