PROGETTAZIONE RAZIONALE DEL FARMACO
Academic Year 2026/2027 - Teacher: SALVATORE GUCCIONEExpected Learning Outcomes
Expected Learning Outcomes – Dublin Descriptors
By the end of the course, students will have acquired knowledge and skills in the use of molecular modelling and computational methods in medicinal chemistry, with particular emphasis on the identification and characterization of potential drug candidates, from target identification and validation to Hit and Lead selection and validation, and the analysis of structure–activity relationships.
1. Knowledge and understanding
Students will acquire a basic understanding of the main molecular modelling approaches applied to biological systems and of their underlying theoretical principles. In particular, they will understand the molecular basis of intermolecular interactions, with specific reference to ligand–receptor recognition, as well as the principles of rational drug design supported by computational methods. They will also understand how molecular models, structural properties, experimental data and biological activity are related and how these relationships can be interpreted.
2. Applying knowledge and understanding
Students will be able to apply the main molecular modelling methods to specific problems in medicinal chemistry. They will be able to analyse a given scientific problem, identify the most appropriate computational approaches according to the system under investigation, and implement them for the analysis and design of molecular systems. They will also be able to apply computational approaches to the investigation of ligand–receptor interactions, the identification and characterization of Hit and Lead compounds, and the analysis of structure–activity relationships.
3. Making judgements
Students will develop the ability to critically evaluate different molecular modelling methodologies, identifying their potential, limitations, advantages and disadvantages in relation to a specific scientific problem. They will be able to make informed choices regarding the most appropriate computational approach, critically assess the reliability of the results obtained, and interpret computational findings in conjunction with available experimental data. They will also be able to critically evaluate statistical results, predictive models and structure–activity relationships.
4. Communication skills
Students will be able to clearly and appropriately describe and discuss the principles, methodologies and results of molecular modelling studies. They will be able to present and interpret computational and experimental data using the appropriate scientific terminology of medicinal chemistry and molecular modelling, and to critically discuss the conclusions derived from their analyses.
5. Learning skills
Students will develop the ability to independently plan and conduct a molecular modelling study by integrating theoretical knowledge, computational methodologies and experimental information. They will be able to identify appropriate methodologies and computational tools for investigating new scientific problems, critically analyse the relevant scientific literature, and update their knowledge in response to developments in computational methods applied to drug discovery and drug design.
Course Structure
Frontal Lessons (6 CFU).
According to "Regolamento Didattico di Ateneo (R.D.A.) i.e. University Didactic Regulations attendance of lessons is mandatory.
Attending the supplementary seminars that are organized is also strongly recommended.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Information for students with disabilities and / or SLD: To guarantee equal opportunities and in compliance with the laws in force, interested students can ask for a personal interview in order to plan any compensatory and / or dispensatory measures, based on the teaching objectives and specifications needs. It is also possible to contact the CInAP contact person (Center for Active and Participatory Integration - Services for Disabilities and / or SLD) of the Department of Chemica Sciences prof. Vera Muccilli.
Required Prerequisites
Attendance of Lessons
Frontal Lessons.
Attendance at the course is mandatory. Students are required to attend at least 70% of the total course hours (see the Academic Regulations, Section 3.1).
Should teaching be carried out in mixed
mode or remotely, it may be necessary to introduce changes with respect to
previous statements, in line with the programme planned and outlined in the
syllabus.
Detailed Course Content
- Process of action of drugs. Pharmacodynamics: molecular targets: interactions between bio-active molecules and drug targets. Pharmacokinetics: adsorption, distribution, metabolism, elimination.
- Introduction to basic principles of protein(target)-ligand interactions and a number of concepts in modern drug discovery.
- Rational drug design and introduction to computational methods.
- Conformational analysis: Geometry optimization and Energy Minimization methods. Quantum- and Molecular-mechanics methods (Force Field).
- Commercial(Cambridge Structural Database: CSD) and non-profit (Protein Brookhaven Databank: PDB) crystallographic databases.
- Structure based methods, binding site analysis, docking, scoring functions and virtual screening.
- Application of docking techniques to the prediction of drug-target interactions.
- MIF methods : GRID, CoMFA.
- Ligand based design approaches including “traditional” (2D) QSAR (QSPR), 3D-QSAR , Pharmacophore modelling.
- Introduction to chemioinformatics and Drug Development.
- Chemical and Drug Databases.
- Property calculations and property filtering.
- Molecular Similarity.
- Prediction of ADME (Absorption-Distribution-Metabolism-Excretion) and toxicity of Drug molecule.
- Chemometry (MLR,PCA,PLS).
- Structural Bioinformatics in Drug Development (Protein Homology modeling).
- Molecular Dynamics.
Textbook Information
Comprehensive Approaches in Computer-Aided Drug Design: QSAR, Docking, Screening, Homology, Pharmacophore and AI-Driven Insights
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Course ProgrammeThe course is organized into thematic modules aimed at providing students with the theoretical knowledge and methodological skills required to understand and apply the main approaches of molecular modelling and computational chemistry to drug discovery and drug design.1. Introduction to molecular modelling and drug discoveryGeneral principles of drug discovery and drug design.Identification and validation of biological targets.Identification, selection and validation of Hit and Lead compounds.Computational strategies supporting different stages of the drug discovery process.Examples of molecular modelling applications in medicinal chemistry.2. Theoretical foundations of molecular modellingPhysicochemical principles underlying intermolecular interactions.Ligand–receptor interactions and molecular recognition.Representation and description of molecular structure.Fundamental concepts of molecular mechanics and quantum mechanics applied to molecular systems.Potential energy functions and evaluation of molecular structural and energetic properties.3. Computational methods for the study of ligand–receptor interactionsStructural analysis of proteins and ligands.Preparation of molecular systems for computational analyses.Molecular docking methods.Analysis of ligand–receptor interactions and evaluation of binding poses.Principles and applications of virtual screening approaches.4. Structure–activity relationships and rational drug designDescription of structural and molecular properties relevant to biological activity.Structure–activity relationships (SAR).Introduction to quantitative approaches for the analysis of structure–activity relationships.Analysis and interpretation of experimental and computational data.Application of computational approaches to support the design and optimization of new drug candidates.5. Advanced computational strategiesIntroduction to advanced modelling approaches applied to drug design.Comparison of different computational methodologies and criteria for selecting the most appropriate approach.Evaluation of the potential, limitations and reliability of different computational methods.Integration of complementary computational approaches to address complex scientific problems.6. Statistical analysis, interpretation and validation of resultsPrinciples of statistical analysis applied to molecular modelling data.Assessment of model quality and reliability.Interpretation of computational results and comparison with experimental data.Correlation between computational results and biological activity.Critical discussion of results and identification of the limitations of the analysis.7. Applications and practical activitiesTheoretical knowledge will be complemented by practical exercises and/or case studies addressing problems of relevance to medicinal chemistry. Students will be guided through the definition of a scientific problem, selection of the most appropriate computational methodology, execution of the analysis, and critical interpretation of the results.Particular emphasis will be placed on the integration of structural, computational and experimental information and on the use of such information to formulate hypotheses supporting the design and optimization of potential drug candidates.8. Development of a molecular modelling studyIn the final part of the course, students will be involved in planning and developing a molecular modelling study, from problem definition and methodological selection to data analysis, interpretation and discussion of the results. This activity will be aimed at developing students’ ability to independently design a computational study and critically evaluate the results obtained. | Comprehensive Approaches in Computer-Aided Drug Design: QSAR, Docking, Screening, Homology, Pharmacophore and AI-Driven Insightshttps://doi.org/10.61096/978-81-990998-7-6 |
Learning Assessment
Learning Assessment Procedures
Oral Exam
Fail: the student does not demonstrate the minimum knowledge required of the main contents of the course. The ability to use the specific scientific terminology is poor or absent, and the student is unable to independently apply the acquired knowledge to the solution of simple problems or to the analysis of the situations presented.
Grade 18–21: the student demonstrates a minimum knowledge of the main contents of the course. The ability to integrate and critically analyse the situations presented is limited, as is the ability to apply the acquired knowledge to simple problems. The presentation is sufficiently clear, although the command and appropriate use of scientific terminology remain limited.
Grade 22–25: the student demonstrates a fair knowledge of the main contents of the course, although it is mainly limited to the fundamental topics. The student is able to integrate and critically analyse the situations presented, although the analysis is not always fully coherent or well structured. The student demonstrates a fair ability to apply the acquired knowledge and to address problems of moderate complexity. The presentation is reasonably clear and characterised by a satisfactory command of scientific terminology.
Grade 26–28: the student demonstrates a good knowledge of the course contents and is able to integrate and critically analyse them in a coherent and structured manner. The student demonstrates a good ability to independently apply the acquired knowledge to the solution of complex problems and to critically interpret data and results. The presentation is clear, well organised and uses appropriate scientific terminology.
Grade 29–30 and honours (cum laude): the student demonstrates an in-depth and comprehensive knowledge of the course contents. The student is able to promptly and correctly integrate the acquired knowledge and to critically and rigorously analyse the situations presented. The student demonstrates a high degree of independence in applying knowledge to the solution of complex problems, critically interpreting data and results, and establishing appropriate connections between theoretical, methodological and applied aspects. The presentation is clear, rigorous and well structured, with an excellent command of the specific scientific terminology of the discipline. Honours (cum laude) are awarded to students who demonstrate an exceptionally comprehensive and in-depth preparation, together with an excellent ability to critically analyse, integrate and independently elaborate on the acquired knowledge.
Learning assessment may also be carried out on line, should the conditions require it.
Examples of frequently asked questions and / or exercises
Frequent Questions:
- Descriptors and scaling