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

Textbook: 

• Leszczynski, Handbook of Computational Chemistry, Springer

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

Students will be tested through an oral exam.

The candidate will be asked to explain and briefly comment on a scientific paper previously selected and agreed upon. A list of suitable scientific papers will provided at the end of the module. Starting from this presentation, more broad and open questions will be asked in order to ascertain the student's knowledge of the entire curriculum.

Final Mark Interpretation 

• 28 - 30 cum laude: the student has achieved a broad and critical understanding of the molecular modelling techniques (how an approach works, its theoretical background, its main implementations) presented in the module. The student understands each technique's advantages and limitations and has a clear grasp on when a specific technique should be used along the drug discovery pipeline. The student masters the discipline's terminology. When confronted with simple case studies, the student can independently and correctly suggest the proper molecular modelling strategy to adopt.
• 24 - 27: as above, however the student's understanding of the molecular modelling techniques (how an approach works, its theoretical background, its main implementations) presented in the module is not always 100% accurate. The student understands with only minor limitations each technique's advantages and limitations. The student has good command of the discipline's terminology. When confronted with simple case studies, the student can suggest the proper molecular modelling strategy to adopt as long as some guidance is provided.
• 18 - 23:  as above, however the student's understanding of the molecular modelling techniques (how an approach works, its theoretical background, its main implementations) presented in the module is still limited. The way the student understands each technique's advantages and limitations is limited and sometimes inaccurate. The student has only limited command of the discipline's terminology. The student's knowledge is limited to notions and when confronted with simple case studies, the student struggles to identify the proper molecular modelling strategy to adopt even if guidance is provided.
• Fail: the student lacks a proper understanding of the theoretical background and of the main implementations of even the most fundamental techniques in modelling. The students has not (or barely has) learnt the subject's proper terminology. 

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

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