Vai al contenuto principale
Oggetto:
Oggetto:

COMPUTATIONAL DRUG DISCOVERY

Oggetto:

COMPUTATIONAL DRUG DISCOVERY

Oggetto:

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.
Oggetto:

Sommario insegnamento

Oggetto:

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 

Oggetto:

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.

Oggetto:

Modalità di insegnamento

The lessons will be given in form of frontal and practical lessons

Oggetto:

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

Oggetto:

Attività di supporto

Classroom practice

Oggetto:

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

Oggetto:

.



Oggetto:

Note

Readings and bibliography will be suggested during the lessons

Oggetto:
Ultimo aggiornamento: 24/09/2021 15:32
Non cliccare qui!