Computational Drug Discovery

Recommended Prerequisites

Basic knowledge in the structure and function of proteins and other biomolecules.

Teaching Methods

The teaching methods are based on Lectures, Practical Classes and Seminars. In the Lectures the theoretical basis of each topic in the syllabus will be presented. The practical classes will be devoted to the development of practical skills in the use of relevant software. Additionally, the students will present a series of seminars related to main topics of the course. 

Learning Outcomes

The present course is designed to provide the student knowledge in the most important computational methodologies used in the area of computational drug discovery. Particularly, the theoretical basis of these methodologies and their limitations. It is also intended to provide the student with practical skills in applying some of those methodologies to solve practical problems or specific projects. 

Work Placement(s)

No

Syllabus

1. Overview of the drug discovery pipeline

2. Target identification

- Main biological targets in Pharmaceutics

- Molecular interactions

- Target modeling: structure modeling, molecular dynamics, QM/MM

- Target fishing

3. Lead discovery

- Virtual screening

- Virtual libraries

- Structure-based virtual screening (SBVS): docking, pharmacophore models

- Ligand-based virtual screening (LBVS): similarity searches, molecular fingerprints, molecular interaction fields

- Performance metrics in virtual screening campaigns 

4. Lead optimization

- Molecular descriptors

- QSAR/QSPR

- Classic approaches, machine learning and deep learning approaches 

5. Drug repurposing

6. Toxicity prediction.

Head Lecturer(s)

Paulo Eduardo Martins de Castro Neves de Abreu

Assessment Methods

Assessment
Other: 15.0%
Project: 25.0%
Exam: 60.0%

Bibliography

Dada a progressão rápida de metodologias na área, em cada ano lectivo será fornecido um conjunto de referências bibliográficas com base em artigos de revisão acessíveis publicamente.