Computer-Aided Drug Discovery

Code:

Q/BIOQ3001

Acronym:

Q/BIOQ3001

Instance: 2025/2026 - 1S

Active?	Yes
Responsible unit:	Department of Chemistry and Biochemistry
Course/CS Responsible:	Bachelor in Bioinformatics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:BIOINF	19	Official Study Plan	3	-	6	48	162

Teaching language

Portuguese

Objectives

To understand the general principles of medicinal chemistry. 
Know the line of discovery and development of drugs.
Know the primary natural sources of therapeutic compounds and the most common synthetic methods to obtain them.
Understand the physical basis of the affinity of a ligand for its macromolecular target and the strategies to increase its affinity.
Know the physicochemical requirements that a drug must possess to have good absorption and oral distribution.
Know the most common metabolic pathways that affect drugs and how these metabolic pathways can be used to activate or inactivate drugs.
Know the most common computational methods used in drug discovery.

Learning outcomes and competences

Be able to perform autonomously and critically the virtual search for ligands for a receptor of interest and refine the affinity and pharmacokinetic properties of the ligand using computational tools.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Not applicable.

Program

LECTURES:
1.     Introduction to pharmaceutical and medicinal chemistry.
2.    Drug discovery and development pipeline.
3.    Molecular recognition.
4.    Physicochemical properties influencing pharmacokinetics and pharmacodynamics.
5.    Drug metabolism.
6.    Strategies for the discovery and development of new drugs.
7.    Hit-to-lead chemistry 
8.    Case studies of computer-assisted drug discovery.

PRACTICAL CLASSES:
1.     Receptor selection.
2.     Receptor preparation.
3.    Construction of pharmacophores.
4.    Virtual scanning of libraries of compounds with molecular similarity criteria.
5.    Selection of most promising ligands based on virtual screening results.
6.    Molecular docking of the ligands into the receptor.
7.    Molecular modeling of ligands- introduction of substituents to increase affinity.
8.    Prediction of ADMET properties of ligands.

Mandatory literature

Patrick , Graham L.; An introduction to medicinal chemistry. ISBN: 0 19 850533 7
Erland Stevens ; Medicinal Chemistry: The Modern Drug Discovery Process, Pearson Education, 2014. ISBN: 9780321710482
Nogrady , Thomas; Medicinal chemistry : a biochemical approach. ISBN: 0-19-505369-9
Thomas , Gareth; Medicinal chemistry: an introduction. ISBN: 0-471-48935-2

Teaching methods and learning activities

The teaching method will be problem-based. The lectures will teach general knowledge about the area. The laboratory component addresses a single central problem - discovering and perfecting a ligand for a pharmacological target of interest. The search for the solution to the problem leads the student to experiment and apply all the techniques intended to be mastered in a successive way.

Software

SwissADME: http://www.swissadme.ch
PyMol: https://pymol.org/
SeamDock: https://bioserv.rpbs.univ-paris-diderot.fr/services/SeamDock

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Apresentação/discussão de um trabalho científico	50,00
Teste	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Elaboração de projeto	24,00
Estudo autónomo	90,00
Frequência das aulas	48,00
Total:	162,00

Eligibility for exams

The student must assist to 75% of the practical classes at least.

Calculation formula of final grade

Evaluation method: Final grade = 50% PE + 50% EX
PE- Oral presentation of the practical works.
EX- Final Theoretical Test or Exam

Special assessment (TE, DA, ...)

Students with a special regime can do a single practical exam at the end of the semester to obtain the practical grade.

Classification improvement

Only the theoretical component of the classification can be improved by repeating the final exam.