Scheda programma d'esame
ADVANCED COMPUTER-AIDED DRUG DESIGN
TIZIANO TUCCINARDI
Anno accademico2021/22
CdSCHIMICA E TECNOLOGIA FARMACEUTICHE
Codice340CC
CFU6
PeriodoSecondo semestre
LinguaItaliano

ModuliSettoreTipoOreDocente/i
ADVANCED COMPUTER-AIDED DRUG DESIGNCHIM/08LEZIONI58
TIZIANO TUCCINARDI unimap
Obiettivi di apprendimento
Learning outcomes
Conoscenze

Lo studente avrà acquisito conoscenze in merito alle tecniche avanzate di Computer-aided Drug Design.

Knowledge

The student will acquire knowledge about the computational chemistry applied to the Drug Discovery field.

Modalità di verifica delle conoscenze

La verifica delle conoscenze sarà oggetto della valutazione della tesina sperimentale che gli studenti porteranno a termine alla fine del corso.

Assessment criteria of knowledge

The student will be assessed on his/her demonstrated ability to carry out a full computational research study.

Methods:

  • Laboratory report

 

Capacità

Lo studente saprà utilizzare il software di studi computazionali

 

Skills

The student who completes the course successfully will be able to demonstrate a solid knowledge of the advanced understanding of ligand-protein interactions. He/she will be familiar with a broad range of ligand- and structure-based computational methods and finally he/she will be able to perfom computational modeling tasks using state of the art software

Modalità di verifica delle capacità

Lo studente dovrà preparare e presentare una relazione scritta che riporti i risultati dello studio computazionale che porterà a termine.

Assessment criteria of skills

The abilities acquired by student will be verified by means of the development of a small research study.

Comportamenti

Lo studente potrà acquisire e/o sviluppare capacità di problem-solving e imparare ad utilizzare applicativi scientifici informatici

 

Modalità di verifica dei comportamenti

Durante le sessioni di laboratorio saranno valutati i risultati ottenuti dalle attività svolte

Prerequisiti (conoscenze iniziali)

Chimica computazionale di base

Prerequisites

Basis of computational chemistry. Furthermore, a good command of the English language is required, since lectures are given in English.

Indicazioni metodologiche
  • modo in cui si svolgono le lezioni: lezioni frontali, con ausilio di slide a disposizione degli studenti
  • modo in cui si svolgono le esercitazioni in aula/laboratorio: esercitazioni singole utilizzando i PC dell'aula informatica
  • tipo di strumenti di supporto: sito web (moodle)
  • tipo di uso del sito di elearning del corso: scaricamento materiali didattici, comunicazioni docente-studenti,  formazione di gruppi di lavoro
  • tipo di interazione tra studente e docente: uso di ricevimenti, uso della posta elettronica, telefono, skype
  • uso parziale o totale di lingue diverse dall'italiano: uso della lingua inglese
Teaching methods

Delivery: face to face

Attendance: Mandatory

Learning activities:

  • attending lectures
  • Laboratory work

 

Teaching methods:

  • Lectures
  • laboratory

 

Programma (contenuti dell'insegnamento)

AIM
The course aims at providing the students with understanding of computational modeling in the area of drug discovery. After finishing the course the students will have:
• Advanced understanding of ligand-protein interactions.
• Be familiar with a range of ligand and structure based computational methods.
• Performed computational modeling tasks using state of the art software.


DESCRIPTION
Although no single drug has been designed solely by computer techniques, the contribution of these methods to drug discovery is no longer a matter of dispute. Allthe world’s major pharmaceutical and biotechnology companies use computational design tools. Computer-aided drug design represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selectionand sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in-silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this course the bases of the computer-aided drug design will be explored, and the lectures will be accompanied by laboratory exercises.

COURSE OUTLINE

a) Molecular dynamic simulations;

b) Pharmacophore-based drug desig;

c) QSAR, 3D-QSAR and in silico ADME studies

d) Artificial intelligence methods applied to the drug discovery field.

Syllabus

AIM
The course aims at providing the students with understanding of computational modeling in the area of drug discovery. After finishing the course the students will have:
• Advanced understanding of ligand-protein interactions.
• Be familiar with a range of ligand and structure based computational methods.
• Performed computational modeling tasks using state of the art software.


DESCRIPTION
Although no single drug has been designed solely by computer techniques, the contribution of these methods to drug discovery is no longer a matter of dispute. Allthe world’s major pharmaceutical and biotechnology companies use computational design tools. Computer-aided drug design represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selectionand sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in-silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this course the bases of the computer-aided drug design will be explored, and the lectures will be accompanied by laboratory exercises.

COURSE OUTLINE

a) Molecular dynamic simulations;

b) Pharmacophore-based drug desig;

c) QSAR, 3D-QSAR and in silico ADME studies

d) Artificial intelligence methods applied to the drug discovery field.

Bibliografia e materiale didattico

Non viene consigliato alcun testo, ma il materiale necessario verrà reso disponibile durante lo svolgimento del modulo

Bibliography

There are not recommended readings. The teacher will supply the material during the lessons.

Modalità d'esame

Valutazione di una Tesina

Altri riferimenti web

www.mmvsl.it

Ultimo aggiornamento 23/11/2021 09:14