Teaching Responsibility
LJMU Schools involved in Delivery:
Pharmacy & Biomolecular Sciences
Learning Methods
Lecture
Practical
Workshop
Module Offerings
7117PHASCI-JAN-CTY
Aims
To enable students to create and curate chemical datasets, using appropriate quality assessment checks and to use such a dataset to build elementary structure-activity relationship models, cognisant of the principles of good modelling practice.
Learning Outcomes
1.
Select appropriate identifiers to ensure the accuracy of chemical structures and discuss key factors in chemical dataset curation.
2.
Characterise chemicals using appropriate descriptors (physico-chemical properties, fingerprints, metabolic potential) and know how to interpret relevant descriptors
3.
Develop/interpret elementary structure-activity relationship models (e.g. rules-of-thumb and structural alerts)
4.
Demonstrate proficiency in the use of a range of computational tools to generate chemical descriptors, assess similarity and rationally group chemicals for the purposes of read-across.
Module Content
Outline Syllabus:Chemical identifiers – advantages/disadvantages of the different types
Finding and curating chemical information, property and activity data; data sources and reliability
Characterising chemicals: chemical and biological descriptors and tools / resources for obtaining or predicting values. Hydrophobic, steric, electronic and topological descriptors.
Similarity metrics, fingerprints and finding data for “similar” chemicals (including similarity of metabolic profile and metabolite prediction).
Relationships between structure and activity (SAR); rules-of-thumb, structural alerts (development and use).
Read-across: analogue selection using various similarity metrics; performing, justifying and reporting read-across predictions.
Key computational tools and resources associated with the above topics.
Module Overview:
Delivers practical experience and understanding of the computational approaches used to predict toxicity of chemicals using knowledge of chemical structure alone, with the aim of reducing/replacing animal testing.
Delivers practical experience and understanding of the computational approaches used to predict toxicity of chemicals using knowledge of chemical structure alone, with the aim of reducing/replacing animal testing.
Additional Information:The contents of this module link directly with module 7118PHASCI (Computational Methods II: Advanced Predictive Methods). This module will predominantly be delivered prior to the delivery of 7118PHASCI so that the concepts introduced here can be supplemented and augmented in the following module.