Advanced Topics in Bioinformatics


This curricular unit aims at giving a general overview of the major areas of Bioinformatics and Chemoinformatics, providing the students with knowledge on the fundamentals as well as on practical applications in biosciences. The knowledge and skills acquired in this curricular unit will allow the students to have a general understanding of the scientific literature in the area, to be able to (individually) deepen their knowledge in selected areas in bioinformatics, and to use some computational tools to study real‐life problems in biosciences.

General characterization





Responsible teacher

Cláudio Manuel Simões Loureiro Nunes Soares, João Montargil Aires de Sousa


Weekly - 5

Total - 56

Teaching language



Basic computer skills.


1. Leach, A. R., Molecular Modelling: Principles and Applications, 2nd ed., Prentice Hall, 2001
2. Bioinformatics and Molecular Evolution by Paul G. Higgs and Teresa K. Attwood. Wiley-Blackwell (ISBN-13: 978-1405106832)
3. An introduction to systems biology. Design Principles of Biological Circuits. U. Alon. Chapman & Hall/CRC Mathematical & Computational Biology; 2006.
4. Chemoinformatics - a Textbook, Gasteiger, J. Engel, T., Eds.; Wiley-VCH: Weinheim, 2003.
5. Leach, A. R.; Gillet, V. J. An Introduction to Chemoinformatics, 2ª ed.; Springer: Dordrecht, 2007.
6. Handbook of Chemoinformatics: from Data to Knowledge, Gasteiger, J., Engel, T., Eds.; Wiley-VCH: Weinheim, 2003.
7. Key papers from diverse fields.

Teaching method

Classes will be Lectures/problem-solving with computers.

Evaluation consists of class evaluation (50%) and final test/exam (50%). Class evaluation will be based on a computational work. A minimum grade of 9.5 is required in the final test/exam to be approved.

Evaluation method

Evaluation consists of class evaluation (50%) and final test/exam (50%). Class  evaluation  may  be  based on  a computational work. A minimum grade of 9.5 is required in the final test/exam to be approved.

Subject matter

1) Computational genomics and evolution.
2) Computational systems biology.
3) An experimentalist survival guide in computational biology methods – Practical sessions, in a problem solving context.
4) Representation and visualisation of molecular structures.
5) Introduction to molecular mechanics/dynamics.
6) Molecular docking.
7) Protein structure prediction  
8) Quantitative Structure‐Activity Relationships (QSAR). 
9) The role of Chemoinformatics in drug discovery and development


Programs where the course is taught: