Drug Development II

Recommended Prerequisites

Not applicable.

Teaching Methods

Teaching is based on lectures, including a significant debate and participation, and a theoretical-pratical component directed at the application of the techniques.

Learning Outcomes

Develop an overall strategy for rational drug design, and addressing a sequence of common relevant numerical and computational techniques. The students should possess a geral notion of each technique, be able to select those adeqate in each situation, and carry out with the application in simple situations.

Work Placement(s)

No

Syllabus

Introduction and general aspects:
Computational approaches in rational drug development.
Regression techniques:
The simple and multiple linear regression. Regression weights. Minimal square nonlinear.
Experimental design:
The 2 ^ k factorial planning. The main effects and interaction. The compounds schedules. Fractionated schedules and Plackett-Burman.
Administration and properties:
Structure. PKa and solubility. Partition. Stability. Dissolution. Relevance. Profiles and their description.
QSAR techniques:
QSAR / QSPR: the concept. QSAR and 3D-QSAR. The method. Descriptors. Examples.
Molecular Simulation:
Methods of force fields and molecular mechanics. Molecular dynamics (MD). The calculation and interpretation of observable properties. The method of Monte-Carlo (MC). MD vs. MC.
Electronic structure calculations:
Empirical methods, theoretical and semi-empirical. Quantum mechanics. Variational methods and perturbacionais.
Density functional theory.

Head Lecturer(s)

Rui Fausto Martins Ribeiro da Silva Lourenço

Assessment Methods

Evaluation
Synthesis work: 15.0%
Exam: 85.0%

Bibliography

D.C. Young, Computational Drug Design: A Guide for Computational and Medicinal Chemists, Wiley (New York, 2009)