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Course Module Information
Course Modules
MA5118: Advanced Chemoinformatics
Semester 2 | Credits: 5
This module will provide students with both the theoretical foundations and practical experience necessary to perform quantitative structure–activity relationship (QSAR) modelling. Drawing on and integrating learning from related modules in Programming (MA5114) and Statistical Computing (MA5108), students will develop a rich toolkit which will allow them connect experimental measures with a set of chemical descriptors in order to predict biological activity (toxicity) for a given compound.
(Language of instruction: English)
Learning Outcomes
- Describe the different databases and file formats associated with chemical compounds and their molecular descriptors.
- Demonstrate the use of software packages/tools (e.g. webchem, rcdk, Babel, ChemDraw, PaDel) to mine existing databases, visualise compounds and generate molecular descriptors.
- Distinguish between regression and classification models and describe machine learning algorithms for QSAR including: support vector machine (SVM), random forest (RF), and naive Bayes (NB) approaches.
- Perform a QSAR analysis, including appropriate data filtering/curation, feature extraction, and model checking and validation.
Assessments
- Department-based Assessment (100%)
Teachers
- COLLETTE MCLOUGHLIN:
Research Profile |
Email
- Pilib O Broin:
Research Profile |
Email
Note: Module offerings and details may be subject to change.