At the end of the course, students will develop both practical and theoretical skills in rational drug design, emphasising computer-aided and data-driven approaches. Skills acquired will encompass: 1. Data Resources and Target Preparation  Navigate and mine bioinformatics and chemoinformatics databases to retrieve relevant information on molecular targets and ligands.  Acquire introductory skills in protein structure modeling through homology modeling techniques, with emphasis on applications in drug discovery.  Gain a basic understanding of molecular modeling principles and the use of force fields in representing drug–target interactions. 2. Drug Design Methodologies  Apply ligand-based and structure-based approaches to screen, design, and optimize bioactive compounds.  Perform docking simulations and evaluate drug–target interactions, including fast binding affinity estimations.  Develop and interpret pharmacophore models and quantitative structure–activity relationships (QSAR). 3. Pharmacokinetics and Molecular Property Prediction  Understand the principles of pharmacokinetic modeling and apply software tools for ADMET prediction. 4. Emerging Technologies  Gain exposure to machine learning tools applied to molecular property prediction and drug screening, including AI-driven analysis of structure–activity relationships and chemical space exploration. In addition to technical skills, students will develop critical thinking, problem-solving, and scientific communication abilities by completing a guided project. The course encourages independent learning via case studies, hands-on computational tutorials, and structured self-assessment. It also equips students with market-relevant skills applicable to careers in pharmaceutical, biotech, and applied research sectors.

A solid background in basic engineering sciences is essential, especially in physics, mathematics, chemistry, biology, mechanics, and materials science. Although previous experience in molecular biology or pharmacology can be beneficial, it is not essential. The lecturer will cover specific knowledge gaps with focused introductory sessions as required.

• Historical overview of drug discovery • Introduction to modern drug design and development strategies • Bioinformatics, chemoinformatics, and pharmacological databases • Ligand-based and structure-based drug design • Protein homology modeling • Ligand/receptor docking and affinity estimation; basics of drug binding kinetics • Virtual screening approaches • Pharmacophore development and model validation • QSAR (Quantitative Structure–Activity Relationship) methodologies • Pharmacokinetics and ADMET prediction • Basic introduction to molecular mechanics and quantum mechanics/molecular mechanics (QM/MM) methods for analyzing drug–protein interactions • Introduction to artificial intelligence and machine learning tools for molecular property prediction and AI-guided drug screening Hands-on sessions in the computational lab will be dedicated to key areas of drug discovery and design, with particular focus on homology modeling, molecular docking, pharmacophore generation, and structure–activity relationship analysis. These practical tutorials are designed to consolidate theoretical concepts and provide students with applied skills using state-of-the-art tools and workflows.

The course is open to students in either the first or second year of the M.Sc. degree program. Approximately half of the lectures will focus on applied computational tools, complemented by guided hands-on sessions. Personalized tutoring by the instructor or collaborators will support the development of the final project. The course aims to equip students with the ability to apply current and emerging techniques, software, and tools in the field of rational drug design.

The course consists of lectures and hands-on tutorials held in the computational lab. Theoretical concepts will be directly applied through guided exercises, fostering practical understanding and technical autonomy.

D.C. Young, Computational Drug Design: A Guide for Computational and Medicinal Chemists, John Wiley &Sons, 2009 Vasanthanathan Poongavanam, Vijayan Ramaswamy (Ed.), Computational Drug Discovery: Methods and Applications, John Wiley &Sons, 2024 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;

The final assessment is based on a group project (3 to 5 students) focused on solving a rational drug design problem using the computational methods introduced during the course. Each team will prepare a report and deliver an oral presentation of their work (e.g., in PowerPoint format). The oral exam will consist of the team presentation and may include individual questions related to the project and the course topics. Evaluation will be based on the following criteria: 1. Understanding of the theoretical concepts covered in the course. 2. Ability to apply theoretical knowledge to practical problems, as demonstrated through the project. 3. Originality, critical thinking, and clarity in the interpretation and presentation of the work.

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.