The main goal of this module is to introduce the key concepts of molecular modelling and computer-assisted drug design (CADD). CADD is an integral part of all drug discovery pipelines and familiarising themselves with CADD's more common applications strenghten the background of future healthcare professionals.

The discipline of machine learning will also be introduced and its applications to drug discovery discussed in details.

By the end of the module, the student will also be familiar with public data repositories and publicly-available online modelling services.

Frontal Lessons

• Introduction to Molecular Modelling
• Representing Molecules
• The Concept of Molecular Similarity
• Library Design
• Brief Introduction to QM Approaches
• The Force Field
• Ligand-based Approaches
• Structure-based Drug Design:
  • Homology Modelling
  • Docking
  • Virtual Ligand Screening
• Molecular Dynamics
• Introduction to Machine Learning
• Application of Machine Learning to Drug Discovery (Case Studies)

Computer Lab

• Docking and SAR
• Molecular Dynamics: Setting Up and Analysing Simulations
• Machine Learning: Generating New Molecules

No coding skills required

Knowledge and Understanding: devise simulations and modelling exercises that complement and integrate with synthesis, pharmacology and the other disciplines involved in early-stage drug discovery. Understand how modelling can inform rational drug design

Applied Knowledge and Understanding: in particular, understand how molecular modelling can play a key role in hit discovery, guide the SAR exploration, curb the resources necessary to perform a successful lead-op campaign.

Making Judgements: develop a clear understanding of the confidence level that should be attached to computational approaches, based on the specific technique, the computational resources deployed and the experimental info that was fed into the simulation

Communication Skills: lingo can represent a hard barrier to overcome between experimentalists and modellers. Being familiar with the at least basic terminology of CADD places students in a better position to establish fruitful interactions with colleagues in their future working environment.      

Learning Skills: by the end of the module, students should be able to confidently navigate online resources, interpret the results of computational protocols, autonomously run simple simulations  

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Revision sessions will be regularly organised during the course.

Teaching

Frontal Lessons

Attendance

Attendance is strongly encouraged yet not mandatory

Course books

For the exam:

• Slides and course work provided

Extra/For Consultation:

• Leach, Molecular Modelling: Principles and Applications, Pearson
• Leach, Gillet, An introduction to Chemoinformatics, Springer
• Gressling, Data Science in Chemistry, De Gruyter

Assessment

During COVID-19 Pandemic: oral exam only

Disabilità e DSA

Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.

A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.

Teaching

No difference for non-attending students. Note: attendance is strongly encouraged