METODI COMPUTAZIONALI IN CHIMICA FARMACEUTICA

Expected Learning Outcomes

• Acquire knowledge of the principles and methodologies of molecular modeling applied to drug discovery.

• Develop the ability to use computational tools for the analysis of molecular interactions and the design of potential drug candidates.

• Understand the theoretical foundations of structure-based and ligand-based drug design.

• Gain skills in interpreting computational results and integrating them with experimental data.

• Be able to critically evaluate the potential and limitations of rational drug design approaches in pharmaceutical research.

Frontal Lessons.

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 Drug and Health Sciences Prof.ssa Santina Chiechio (santina.chiechio@unict.it).

Office hours for students: Monday at 4:30 p.m. and Friday at 9:00 a.m. Palazzo dell’Etna, Via San Nullo 5A, first floor, room 26

Exam dates: http://www.dsf.unict.it/corsi/lm-13_ctf/calendario-esami

Synopsis: This course will present drug development “Hit to lead selection and validation” as a process involving target selection,  lead discovery and optimization using computer based method. Along the way the  student  will learn about molecular recognition and  computer aided drug design as applied to the development of new drugs.

Course contents and teaching

Principal aims

To introduce students to molecular modelling techniques as applied to biological systems and their underlying theory. The student should gain a basic understanding of the available computational methods and their theoretical foundations; what time scales and length scales are accessible; what properties can be computed and to what level of accuracy; and what methods are the most appropriate for different molecular systems and properties.

Relevant in silico tools along with success stories, possibilities and difficulties.will be also briefly presented.

• 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-ligand interactions and a number of concepts in modern drug discovery.
• Rational drug design and introduction to computational methods.
• Chemoinformatic
• Chemometry (MLR, PCA, PLS).
• Conformational analysis: Geometry optimization and Energy Minimization methods. Quantum- and Molecular-mechanics methods (Force Field).
• Commercial(Cambridge Structural Database: CSD) and non-profit (Protein Brookaven 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 (CoMFA) , Pharmacophore modelling.
• Chemical and Drug Databases.
• Property calculations and property filtering.
• Molecular Similarity.
• Prediction of ADME (Administration-Distribution-Metabolism-Excretion) and  toxicity of Drug molecule.
• Structural Bioinformatics in Drug Development (Protein Homology modeling).
• Molecular Dynamics.

1. TEXTBOOKS AND OTHER RESOURCES

Due to the cutting edge nature of this course and the rapid advances made in the field , a single primary text which adequately covers the content of this  course has not been identified.  Therefore each lecturer will provide the student with additional resources to supplement their lecture material. These resources will take the form of text  books, journal articles (if available links to the electronic form of  these resources will be provided) or web based resources. 

Notes from the class; Chemometry booklet; Useful readings suggested from the Teacher.

Oral Exam

Learning assessment may also be carried out on line, should the conditions require it.

Information for students with disabilities and / or SLD:

To guarantee equal opportunities and in compliance with the laws in force, interested students can ask the staff for an interview in order to plan any compensatory and / or dispensatory measures, based on educational objectives and specific needs.

It is also possible to contact the referent teacher CInAP (Center for Active and Participated Integration - Services for Disabilities and / or SLD) of the Drug and Health Sciences Department,  Prof. Santina Chiechio (santina.chiechio@unict.it).