B5849 - Computational Drug Discovery

Learning outcomes

Upon completion of the course, students will acquire theoretical and practical knowledge of the main computational techniques used in drug discovery and development. Specifically, they will learn the fundamentals and applications of methods such as molecular docking, molecular dynamics, and the use of predictive models based on machine learning and artificial intelligence, as well as strategies for integrating them into the process of identifying and optimizing potential new drugs. Students will also be able to plan in silico studies, set up virtual experiments, and use specialized software tools, both open-source and commercial, to support the design and development of bioactive molecules for various pharmaceutical targets.

Course contents

Frontal lectures (4 CFU: 28 hours)

• Introduction and basic concepts.
  The drug action process: pharmaceutical phase, pharmacokinetics, pharmacodynamics
  Molecular targets of drug action: definition and examples; thermodynamic representation of drug-target interaction. Drug-target interactions: covalent bond and non-covalent interactions, enthalpic and entropic contributions.

• The paradigm of drug discovery.
  The "magic bullet"; examples: penicillins, steroid hormone derivatives, kinase inhibitors.
  Polypharmacology and multi-target drugs.
  Targeting signaling pathways and systems.
  

• In silico drug design strategies.
  Structure-based approaches: Virtual Screening; FBDD (Fragment-Based Drug Design).
  Ligand-based approaches: pharmacophores and database searching; QSAR and chemoinformatics.
  Network-based approaches.

• Molecular modeling.
  Potential energy functions.
  Minimization algorithms.
  Conformational analysis: systematic search and genetic algorithms.
  Fundamental principles of Monte Carlo simulations.
  Practical aspects of molecular docking.
  

• Molecular Dynamics Simulations.
  Fundamental principles of Molecular Dynamics simulations.
  Calculation of experimentally relevant observables for pharmaceutical interest through Molecular Dynamics simulations.

Computational laboratory (2 CFU: 26 hours)

• Hands-on exercises with dedicated software.
  Drawing and construction of small molecules.
  Protein crystal structures for structure-based drug design (Protein Data Bank).
  Setting up and performing molecular docking calculations.
  

Readings/Bibliography

Reading material provided by the teachers during the lectures.

Teaching methods

Frontal lectures and training exercises on the computer.

Considering the type of activities and teaching methods adopted, attendance of this educational activity requires all students to have previously completed Modules 1 and 2 of the safety training for study environments, delivered through e-learning.

Assessment methods

• Students will be required to present and discuss orally a research paper retrieved from the literature reporting a work dealing with an argument inherent to the course contents (computational design/identification of bioactive compounds).
  In addition, further questions inherent to the whole course contents will be asked (including the laboratory).

• Regarding the assessment of learning outcomes, limited, declared, and non-substantial use of AI is permitted for support activities (e.g., summarization and rewording). Substantial use of AI to complete any part of the assessment is not permitted.

• Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Teaching tools

Electronic slides, scientific papers, and other teaching materials available through the Virtuale platform.

Office hours

See the website of Matteo Masetti