Description

This course focuses on databases in chemistry. It explores three main areas: data sources, software tools for data management, and data utilization. The course provides a comprehensive overview of data in chemistry. It then introduces the tools and strategies needed to acquire, organize, access, maintain, and share data. Finally, the data is used in the context of QSAR models as an introduction to machine learning.

·         Data sources explored: 

SciFinder, Reaxys, CSD, PDB, ChEMBL, PubChem, etc. The sources are chosen to illustrate a wide range of applications: organic chemistry, organometallic chemistry, medicinal chemistry, physical chemistry, among others.

·         Specific features of chemical data:

business rules, standardization, etc.

·         Database management:

Tables, relationships, views, forms, data entry, extraction, indexing, backup, and migration.

Illustration using ChemAxon/InstantJChem, Derby, etc. software.

·         Data exploitation:

QSAR classification and regression models. Illustration using Weka and KNIME software.

Compétences visées

Find relevant data sources in chemistry. Extract data and create datasets. Correct and standardize data at a beginner/intermediate level. Create, use, and maintain a database in chemistry at a beginner/intermediate level. Model data using machine learning techniques at a beginner level.

MCC

Les épreuves indiquées respectent et appliquent le règlement de votre formation, disponible dans l'onglet Documents de la description de la formation.

Régime d'évaluation

ECI (Évaluation continue intégrale)

Coefficient

3.0

Évaluation initiale / Session principale - Épreuves