Overall objectives:

1 - Development and improve the knowledge and skills to tackle and solve molecular modeling problems using computational tools.
2 - To get acquainted with methods for the modelling of biochemically relevant systems.
3 - Practice the algorithms used in software applications for molecular modelling in the chemistry and biochemistry areas.
4 - To use the most convenient molecular modelling techniques to describe and predict equilibrium and dynamic properties of molecular, macromolecular and biological systems.
5 - Skills to identify the best approaches to describe and analyse molecular, properties under study.
6 - Acquisition of competence for formulation of hypotheses and methodologies its verification, as well as management of complex activities and taking up responsibility for decision making under unpredictable working conditions.

Syllabus:

1 - Theoretical review: Historical background, Atomic Theory and Chemical Bond; Hardware and Software; Application Areas.
2 - Quantum Mechanics Methods: Molecular Orbitals Theory (MOT); Hartree-Fock and Post-Hartree-Fock methods; Semi-empirical methods; Ab initio and DFT methods.
3 - Molecular Mechanics Methods: Force field equations, basic algorithms and applications.
4 - Modelling of Molecules: Geometry optimization; Energy calculations; Solvation; Chemical reactions.
5 - Biomolecules study: proteins and nucleic acids structure and interactions.
6 - Ligand Design: Computational representation of molecules; molecular docking; similarity-based methods and combinatorial chemistry.
7 - Molecular Modelling Project.

Literature/Sources:

P.W. Atkins , Physical Chemistry , Oxford University Press
Warren J. Hehre , 2003 , A Guide to Molecular Mechanics and Quantum Chemical Calculations , Wavefunction Inc.
David C. Young , 2001 , Computational Chemistry: A Practical Guide for Applying Techniques to Real-World Problems , John Wiley & Sons
M. P. Allen & D. J. Tildesley , 1991 , Computer simulation of Liquids , Clarendon Press
Andrew R. Leach , 2001 , Molecular modelling: Principles and Applications , Prentice Hall

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
The teaching methodology follows a combination of theoretical and practical classes. The theoretical classes allow the study of knowledge. In the practical classes, with use of computers, there is a systematic training of the skills and strategies needed for building and modelling small molecules and more complex systems as well. Practical tasks include simple and more complex ones, directed towards problem solving and case study analysis. Students are encouraged to do an autonomous use of the software and practice with several exercises. The project implementation is split between contact and autonomous components. The assessment is quantitative, with a score from 0 to 20 points, split into two components: project report, oral presentation and discussion (with a weight of 70% on the final mark); a practical exam (weighted as 30%) to evaluate the acquisition of knowledge and skills.