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COMPUTATIONAL DRUG DISCOVERY
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COMPUTATIONAL DRUG DISCOVERY
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Anno accademico 2021/2022
- Codice dell'attività didattica
- BIO0220
- Docenti
- Prof. Giuseppe Ermondi (Titolare del corso)
Prof.ssa Giulia Caron (Titolare del corso) - Corso di studi
- Laurea Triennale in Biotecnologie
- Anno
- 3° anno
- Periodo didattico
- Secondo semestre
- Tipologia
- A scelta dello studente
- Crediti/Valenza
- 4
- SSD dell'attività didattica
- CHIM/08 - chimica farmaceutica
- Modalità di erogazione
- Mista
- Lingua di insegnamento
- Inglese
- Modalità di frequenza
- Obbligatoria
- Tipologia d'esame
- Orale
- Prerequisiti
- Basic knowledge of chemistry and biochemistry. Basic knowledge of computer sciences.
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Sommario insegnamento
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Obiettivi formativi
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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Risultati dell'apprendimento attesi
Students will be able to analyse the 3D structure of proteins paying particular attention to the interaction between drugs and their targets. They will handle experimental and predicted protein structures. Finally, the will learn how applying some web-based and stand-alone tools to resolve simple drug design problems, e.g. the docking of a drug, the definition of a pharmacophore, etc.
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Modalità di insegnamento
The lessons will be given in form of frontal and practical lessons
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Modalità di verifica dell'apprendimento
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.
The final examination is oral and includes a discussion of the reports.
Exams will be conducted 'face-to-face' or online according to university guidelines
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Attività di supporto
Classroom practice
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Programma
- Visualization of drug and protein 3D structures and of their interaction
- Machine learning and Artificial Intelligence applied to Drug Design
- AlphaFold: limits and potentiality of this new resource
- The dynamics of the proteins
- Molecular dynamics strategies
- Structure-based virtual screening
- Molecular docking
- Ligand-based virtual screening
- Pharmacophore definition and application to drug design
- ADME predictors
Testi consigliati e bibliografia
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Note
Readings and bibliography will be suggested during the lessons
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