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Cheminformatics and Toxicology (MSc)

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Course Overview

Cheminformatics is the use of computational techniques to solve chemistry, pharmacology and toxicology problems. Students will understand and apply a range of computational tools to address toxicological questions in preparation for a career in in silico toxicity prediction in the pharma, industry, consultancy, academia and government. The course is delivered over one year by the disciplines of Pharmacology and Therapeutics, Mathematics and Chemistry.

What Makes This Course Unique...

  • Integrated training in toxicology and computational approaches (analytics) to develop a highly marketable skill-set for a career in the Pharma industry or organizations that regulate chemical safety
  • Guest lecturers from regulators and industry that teach from "real-life" cases and that can provide career development advice
  • An independent research project focussed on solving real world toxicity/toxicity assessment problems

Scholarships Available
Find out about our Postgraduate Scholarships here.

Applications and Selections

Applications are made online via the NUI Galway Postgraduate Applications System.  

A short listing procedure will be applied that evaluates:

  • Undergraduate academic performance throughout their time at university
  • The content and quality of their personal statement
  • Prior research or work experience
  • Reference letters

The ideal student will have a BSc or MSc in chemistry with an interest in toxicology, and computational approaches to toxicity prediction. Students with a background in Pharmacology or Bio-informatics (or related disciplines) will also be encouraged to apply.

Who Teaches this Course

  • Dr Howard Fearnhead
  • Dr Declan McKernan
  • Professor John Kelly
  • Professor Cathal Seoighe
  • Dr Pilib Ó Broin
  • Dr Aaron Golden
  • Dr David Cheung

Requirements and Assessment

Key Facts

Entry Requirements

Primary degree: A 2.2 degree or higher (or equivalent) in Chemistry, Pharmacology, Biochemistry or a related discipline.

Language skills: An IELTS score of 6.5 or greater in all categories is required.


Additional Requirements

Duration

1 year, full-time

Next start date

September 2020

A Level Grades ()

Average intake

6

Closing Date

 Please view the offer rounds website.

NFQ level

Mode of study

ECTS weighting

90

Award

CAO

Course code

1CIT1

Course Outline

The course is delivered over three semesters. In semester 1 students learn the fundamentals of pharmacology, toxicology and are introduced to computational drug-design, programming for biology and statistical computing in R. This forms a foundation for more advanced material explored in Semester 2. 

In Semester 2 students consider more advanced concepts in toxicology and investigate controversial areas of toxicology. They also develop a theoretical and a practical understanding of high through put and high content screening technologies that are used to generate large data sets for analysis.  The students also learn to apply bioinformatic and cheminformatic tools to such large data sets.  This semester equips the students to develop and test a novel hypothesis through independent research that is completed in the third semester. 

In the third semester students work independently but with the guidance of an academic or industry-based thesis supervisor on a cheminformatics research project. 

The course involves lectures, laboratory-based training, self-directed learning and a three month independent research project. Competence is assessed through a mixture of written examinations, computer-based examinations, course work (including verbal presentations and poster presentations) and a research thesis.

Curriculum Information

Curriculum information relates to the current academic year (in most cases).
Course and module offerings and details may be subject to change.

Glossary of Terms

Credits
You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
Module
An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
Optional
A module you may choose to study.
Required
A module that you must study if you choose this course (or subject).
Semester
Most courses have 2 semesters (aka terms) per year.

Year 1 (90 Credits)

Required PM208: Fundamental Concepts in Pharmacology


Semester 1 | Credits: 5

This module introduces students to fundamental pharmacological concepts of pharmacodynamics and pharmacokinetics. A combination of lectures, tutorials and workshops will be used.

Learning Outcomes
  1. describe the main drug targets
  2. interpret dose response curves for agonists, antagonists, inverse agonists
  3. calculate molarities, concentrations, volumes required in making solutions
  4. access and critically analyse and interpret pharmacological data
  5. describe the processes of absorption, distribution, metabolism and excretion for specific drugs
  6. explain the effects of different routes of administration on absorption of drugs, and effects of food and drug interactions on drug disposition
  7. derive pharmacokinetic data and use them to predict clinical properties of drugs
Assessments
  • Continuous Assessment (30%)
  • Computer-based Assessment (70%)
Module Director
Lecturers / Tutors
Reading List
  1. "Pharmacology" by Rang, H.P., Dale, Ritter, Flower & Henderson
    Publisher: Churchill Livingstone
  2. "Principles of Pharmacology" by Golan, D.E., et al
  3. "Lippincott’s Illustrated Reviews Pharmacology" by Harvey, R.A.
The above information outlines module PM208: "Fundamental Concepts in Pharmacology" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Required PM311: Introduction to Toxicology


Semester 1 | Credits: 5

A 5ECTS module developed to provide an introduction to Toxicology to third year science students who have an interest in poisons and a background in Pharmacology, Biochemistry, Physiology, Anatomy or Chemistry. The course involves lectures delivered over one semester and is assessed through continuous assessment and a 2 hour written examination at semester's end.
(Language of instruction: English)

Learning Outcomes
  1. use the language, terms, and definitions of toxicology
  2. describe the factors affecting toxic responses
  3. describe specific mechanisms of toxic action
  4. apply this knowledge to explain specific examples of target organ toxicity
  5. describe how toxicity assessed and the challenges of risk assessment
  6. collect toxicological information and apply toxicological principles to specific classes of toxicant and specific situations
Assessments
  • Continuous Assessment (40%)
  • Computer-based Assessment (60%)
Module Director
Lecturers / Tutors
Reading List
  1. "Casarett & Doull's Essentials of Toxicology" by n/a
    Publisher: McGraw-Hill Professional
  2. "Principles of Biochemical Toxicology" by n/a
The above information outlines module PM311: "Introduction to Toxicology" and is valid from 2016 onwards.
Note: Module offerings and details may be subject to change.

Required MA5108: Statistical Computing with R


Semester 1 | Credits: 5

This is a self-contained module designed to train the student into the use of applied statistical techniques in genomics. It overlaps with content in MA5111 (Genomics Data Analysis 1) reinforcing the use of R in genomics contexts. It consists of three parts (i) hypothesis testing (ii) statistical modeling and (iii) fundamentals in statistical computing. Extensive use is made of R in demonstrating and illustrating statistical concepts, and all examples are genomics related.
(Language of instruction: English)

Learning Outcomes
  1. Implement statistical solutions using the R statistics software ecosystem
  2. Understand the statistical properties of data & how to implement hypothesis testing
  3. Understand the sensitivity, specificity and power of a test, and how to determine these parameters
  4. Implement statistical models constructed from genomics datasets in R
  5. Understand and deploy linear and general linear models in R, including logistic regression and survival analysis
  6. Use R Markdown to prepare professional quality statistical reports, including appropriate plots/graphics
Assessments
  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module MA5108: "Statistical Computing with R" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Required PM5108: Applied Toxicology


Semester 1 | Credits: 5

Self-directed learning module to apply the principles of toxicology to the assessment of toxicological catastrophes, individual toxicants and classes of toxicants

Learning Outcomes
  1. Collect/collate toxicological data on specific toxicants or classes of toxicants
  2. Interpret toxicological data
  3. Apply knowledge of toxicity assessment including challenges faced in extrapolating risks to man to interpret risk posed by specific toxciants or classes of toxicants
  4. Apply knowledge of the factors affecting toxic responses, specific mechanisms of toxic action, and knowledge of target organ toxicity to specific toxicants or classes of toxicants
Assessments
  • Department-based Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module PM5108: "Applied Toxicology" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Required MA5114: Programming for Biology


Semester 1 | Credits: 5

This module provides postgraduate students with foundation and applied programming skills using the Python ecosystem. Topics include flow control, conditional statements, regular expressions and direct interaction/manipulation of the operating system and other programs within Python. Applied activities will involve use of notebooks, plotting and data analytics packages, including the BioPython framework.
(Language of instruction: English)

Learning Outcomes
  1. Access, interpret and apply programming education resources
  2. Formulate an algorithm to solve a problem using computational data
  3. Implement a given algorithm using Python and its associated libraries
  4. Understand how to represent and process, DNA, proteomic and other data formats using Python
  5. Combine third party programs, mediated by a Python scripts, to create pipelines/workflows
Assessments
  • Continuous Assessment (100%)
Module Director
Lecturers / Tutors
Reading List
  1. "Python for Biologists: A complete programming course for beginners" by n/a
    ISBN: 978-149234613.
    Publisher: CreateSpace Independent Publishing Platform; 1 edition (September 7, 2013)
The above information outlines module MA5114: "Programming for Biology" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required CH5106: Computational Approaches to Drug Design and Biomolecular Structure


Semester 1 | Credits: 5

In this module computational methods for the investigation of drug design and biomolecular structure and function will be taught. It will consist of theory and practical components. The theory component will outline the main computational methods and models and give examples of their application. The practical component will consist of computer based practical classes, aiming to illustrate the main points of the theory component and give the students hands-on experience of their application.
(Language of instruction: English)

Learning Outcomes
  1. Understand classical mechanical force fields and their use in describing molecular structure
  2. Understand molecular docking and molecular dynamics calculations and their application in understanding drug discovery and biomolecular structure
  3. Relate concepts in molecular mechanics to thermodynamic properties of ligand-protein interactions (enthalpy, entropy, the role of solvent)
  4. Be competent in using computational software to model biomolecules
Assessments
  • Written Assessment (50%)
  • Continuous Assessment (50%)
Module Director
Lecturers / Tutors
The above information outlines module CH5106: "Computational Approaches to Drug Design and Biomolecular Structure" and is valid from 2018 onwards.
Note: Module offerings and details may be subject to change.

Required PM5114: Screening Molecular Libraries


Semester 2 | Credits: 5

The module will provide training in high-throughput and high-content screening technologies to post-graduate students. The course delivers training through a mixture of lectures, practical classes, tutorials, assignments and a training workshop. Students will gain theoretical and practical knowledge of high-throughput and high-content screening and develop proficiency in a range of data analysis techniques. The course will be delivered through the Biomedical Sciences Screening Core Facility at NUI Galway. The facility is fully equipped to deliver all aspects of the course. The course will be open to research MSc students and PhD students in biomedical sciences (College of Science, College of Medicine and other relevant Colleges ) subject to capacity and approval of the Module owner.
(Language of instruction: English)

Learning Outcomes
  1. Demonstrate a detailed knowledge of the principles and concepts of screening.
  2. Demonstrate an in-depth knowledge of the recent developments and applications in the field of screening
  3. Demonstrate a competency in a wide range laboratory skills relevant to high-throughput and high content screening activity
  4. Identify the key features important when designing new screens,
  5. Be able to conduct a screen proficiently and to appropriately analyse and summarise screening data.
Assessments
  • Continuous Assessment (10%)
  • Oral, Audio Visual or Practical Assessment (90%)
Module Director
Lecturers / Tutors
The above information outlines module PM5114: "Screening Molecular Libraries" and is valid from 2018 onwards.
Note: Module offerings and details may be subject to change.

Required 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
  1. Describe the different databases and file formats associated with chemical compounds and their molecular descriptors.
  2. Demonstrate the use of software packages/tools (e.g. webchem, rcdk, Babel, ChemDraw, PaDel) to mine existing databases, visualise compounds and generate molecular descriptors.
  3. 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.
  4. Perform a QSAR analysis, including appropriate data filtering/curation, feature extraction, and model checking and validation.
Assessments
  • Department-based Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module MA5118: "Advanced Chemoinformatics" and is valid from 2019 onwards.
Note: Module offerings and details may be subject to change.

Required PM5112: Research Project in Toxicology


Trimester 3 | Credits: 30

This is a 12-week individual laboratory-based research project. The aim of this module is to provide the students with experience of conducting scientific research as well as communicating their research via oral presentation and written dissertation.
(Language of instruction: English)

Learning Outcomes
  1. Design scientific experiments to address a specific research question.
  2. Demonstrate technical skill and competency in relevant scientific procedures.
  3. . Work independently, responsibly and safely in the laboratory.
  4. Generate, analyse, depict and critically interpret scientific data.
  5. Critically review relevant historical and state-of-the-art scientific literature.
  6. Communicate scientific findings through appropriate verbal, written and visual means.
Assessments
  • Research (100%)
Module Director
Lecturers / Tutors
The above information outlines module PM5112: "Research Project in Toxicology" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Required MA324: Introduction to Bioinformatics (Honours)


Semester 2 | Credits: 5

The course will give students an appreciation of the application of computers and algorithms in molecular biology. This includes foundation knowledge of bioinformatics; the ability to perform basic bioinformatic tasks; and to discuss current bioinformatic research with respect to human health.

Learning Outcomes
  1. outline key bioinformatics principles and approaches
  2. discuss the relevance of bioinformatics to medicine
  3. obtain molecular sequence data from public repositories
  4. implement key bioinformatics algorithms by hand on toy datasets
  5. use bioinformatics software tools, including tools for sequence alignment, homology searching, phylogenetic inference and promoter analysis;
  6. describe key high throughput data generation technologies and the steps involved in data pre-processing and basic analysis of these data.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Module Director
Lecturers / Tutors
Reading List
  1. "Bioinformatics ; Sequence and Genome Analysis" by David W Mount
    ISBN: 9788123909981.
    Publisher: CBS Publishers & Distributors
  2. "INTRODUCTION TO BIOINFORMATICS." by Arthur M. Lesk
    ISBN: 9780195685251.
    Publisher: OUP
  3. "Bioinformatics" by [edited by] Andreas D. Baxevanis, B. F. Francis Ouellette
    ISBN: 9780471383901.
    Publisher: Wiley-Interscience
The above information outlines module MA324: "Introduction to Bioinformatics (Honours)" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Required PM5111: Advanced Toxicology


Semester 2 | Credits: 5

This module is designed to further develop knowledge and understanding of advanced topics in toxicology.
(Language of instruction: English)

Learning Outcomes
  1. To critcally assess and discuss recent advances in the filed of Toxicology
  2. To Interpret toxicological data from the literature on specific toxicants and assess its potential risk to human health or the environment
Assessments
  • Written Assessment (100%)
Module Director
Lecturers / Tutors
Reading List
  1. "Casarett & Doull's Essentials of Toxicology" by n/a
    Publisher: Publisher: McGraw-Hill Professional
  2. "Principles of Biochemical Toxicology." by n/a
    Publisher: lnforma Healthcare;
The above information outlines module PM5111: "Advanced Toxicology" and is valid from 2017 onwards.
Note: Module offerings and details may be subject to change.

Required PM5110: Current Topics in Toxicology


Semester 2 | Credits: 10

This is a self-directed assignment-based module that aims to develop students' capabilities in data analysis, interpretation and presentation and to familiarise them with recent advances and controversial topics in the field of toxicology.

Learning Outcomes
  1. . Evaluate the current safety information for a named drug at various stages of its development
  2. Critically analyse the evidence and synthesise an opinion on a controversial topic in toxicology
  3. Develop a research proposal and design experiments to address a project title
Assessments
  • Department-based Assessment (100%)
Module Director
Lecturers / Tutors
The above information outlines module PM5110: "Current Topics in Toxicology" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.

Why Choose This Course?

Career Opportunities

It costs approximately $1bn and 10–20 years to get a drug from conception to market. While many candidate molecules enter the drug development pipeline, most will fail to become drugs, mainly due to unexpected toxicity. The failure to identify toxicity early in the development process costs the pharmaceutical industry billions of dollars in either failed clinical trials or in withdrawing drugs from the market. At the same time national and trans-national regulatory bodies work to identify the toxicity of chemicals used in food-stuffs, consumer products, industry and agriculture with the aim of building a chemically safe society. Consequently the global ADME toxicology testing market, which aims to identify potential toxicity is projected to surpass $16.2 billion by 2024. In an era when Pharma investment in research and development is falling, scientists to develop and use computational tools that better predict toxicity are at a premium. The value of these skills is further enhanced by the scarcity of training programmes to produce toxicologists with the appropriate computational skills. 

Graduates from the course will be employed in the Pharmaceutical industry, the Cosmetics Industry, National and EU Regulatory bodies, Toxicology Consultancies and academia. 

Who’s Suited to This Course

Learning Outcomes

 

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€7,900 p.a. 2020/21

Fees: Tuition

€7,676 p.a. 2020/21

Fees: Student levy

€224 p.a. 2020/21

Fees: Non EU

€16,200 p.a. 2020/21

Find out More

Howard Fearnhead, PhD
T: +353 91 495 240
E: howard.fearnhead@nuigalway.ie
www.nuigalway.ie/our-research/people/howardfearnhead/

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