The student will gain competence in specific computational techniques at the level of molecular target modeling, ligand-protein interactions and pharmacokinetic simulations. To better understand the practical use of these methods, case studies will discuss examples of drug development from oncology, virology and immunology At the end of the course the student will be able to: • Understand molecular modeling approaches and force fields applied to drug-target systems. • Develop homology models of proteins • Perform bioinformatics and chemo-informatics database mining • Employ ligand-based drug screening/discovery/design algorithms • Employ structure-based drug screening/discovery/design algorithms with particular focus on drug-target docking simulations and fast binding affinity predictions • Understand the basis of pharmacokinetic modeling, use software for ADMET prediction, • Develop pharmacophore models, perform a QSAR determination. This course will help students to develop their independent thinking through self-assessment tests. The ability to learn is stimulated by a training program that alternates, in an organized schedule, methodological principles, application examples, and exercises. The course will help to improve both written and oral communication skills through classroom exercises, group and individual tutorials and through the development of a short applied project focused on drug design. The applied project will encourage the students to undertake surveys on websites, to view the scientific literature and to become aware of the applied research areas related to the course. The course will provide students with marketable skills for prospective employment in the biotech and pharma industries.

Good knowledge of the basics of engineering with particular attention to physics, mathematics, chemistry, biology, mechanics, materials science. The lecturer will fill specific background gaps by ad hoc lectures.

• Historical overview of drug discovery • Introduction to modern drug design and development • Bioinformatic, chemi-informatic and pharmacological databases • Drug design and discovery • Protein homology modelling • Ligand/receptor docking for affinity calculation. Drug binding kinetics • Virtual Screening • Pharmacophore development • QSAR methodology • Pharmacokinetics and ADMET prediction • Basic Introduction to Molecular Mechanics, Molecular Dynamics and QM/MM for precise investigation of drug-protein binding interface

The course can be taken in both the first and second year of the M.Sc. degree program. A half of the lectures will be dedicated to computational tools. Personalized tutoring by the teacher or collaborators will be useful for the project development. The idea of the course is to provide the students the ability to employ new techniques, software and tools in the field of drug design.

Lectures and hands on tutorials in the computational lab

• D.C. Young, Computational Chemistry, John Wiley &Sons, 2001 • Leach, A.R., 2001. Molecular modelling : principles and applications. Prentice Hall. • R.D. Hoffmann, A. Gohier and P. Pospisil (eds.) Data Mining in Drug Discovery, Wiley-VCH, 2014 • G. Klebe (ed.), Drug Design: Methodology, Concepts and Mode-of-Action, Springer, 2013

Exam: Group project;

Computational Project to be carried out in team (3 to 5 students). The Project consists in solving a drug design problem by computational methods introduced during the course. Each team will prepare a report on the results of the project carried out in the form of a presentation (e.g. pptx). The exam is a team presentation. During the oral exam, the teacher may ask for information related to the project The teacher will evaluate 1. the student's knowledge of the topics covered during the course 2. the student's ability to apply the theoretical concepts to practical examples (e.g. correlating the theoretical concepts to their application in the group project) 3. the originality and critical thinking of the student in relation to the theoretical arguments/application examples covered during the exam.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.