Department of Chemistry course timetable
January 2020
Tue 28 |
An interactive training workshop to develop your relationship management skills with a specific focus on working effectively with your supervisor. Relationship Management • Manage expectations Communications skills • Challenge Assumptions • Manage difficult conversations • Manage your time together |
This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level. In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning. During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced. For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research. |
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Wed 29 |
This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course. |
Chemistry: DD4 Pharmacokinetics
Finished
Predicting and controlling how a chemical molecule will be processed by the body is vital to developing a successful drug. This lecture will discuss the path a molecule takes from initial dose through to elimination, describe the ADME (Absorption, Distribution, Metabolism and Excretion) processes that take place and how these are related to compound structure and physicochemical properties. In addition to standard small molecule PK some other new modalities will be also be introduced to illustrate how methods such as PEGylation and lipoparticle encapsulation can be employed to modulate compound pharmacokinetic properties. |
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Thu 30 |
This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level. In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning. During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced. For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research. |
Fri 31 |
Chemistry: IS1 Library Orientation
Finished
This is a compulsory session which introduces new graduate students to the Department of Chemistry Library and its place within the wider Cambridge University Library system. It provides general information on what is available, where it is, and how to get it. Print and online resources are included. You must choose one session out of the 9 sessions available. |
February 2020
Mon 3 |
This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course. |
Tue 4 |
This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level. In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning. During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced. For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research. |
A real drug discovery example will be used. After a brief introduction to the task and the chemical startpoint, we will split into teams and iteratively try to design improved analogues. Molecules will be marked “in real time” during the session to recreate the design-make-test-analysis cycle, then teams can compare their optimized molecules, and we can compare them to what happened in real life. Please note: To take part in this session you will need to have attended DD1-DD4. |
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Wed 5 |
This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course. |
Thu 6 |
This compulsory session introduces Research Data Management (RDM) to Chemistry PhD students. It is highly interactive and utilises practical activities throughout. Key topics covered are:
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Fri 7 |
Drug safety remains the primary cause of compound attrition when developing new medicines and consequently the ability to understand and predict toxicity is regarded as high priority within the pharmaceutical sector. This lecture will describe some common safety liabilities and ongoing work to build a greater understanding of the relationships between chemical structure and toxicity risk that are being harnessed to guide the design of safer compounds. |
Mon 10 |
This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course. |
Chemistry: Quantum Computing
Finished
Lecture 1 - Fundamentals of Quantum Computing A short summary of all the basic quantum computing knowledge needed to do quantum chemistry on a quantum computer. Lecture 2 - Encoding chemistry systems in quantum computers
Lecture 3 - Quantum algorithms for energy calculations
Lecture 4 - Advanced quantum chemistry quantum computing algorithms
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Wed 12 |
Kinase drug discovery remains to be an area of significant and growing interest across academia and in the pharmaceutical industry - there are approximately 30 FDA approved small molecule inhibitors which target kinases, half of which were approved in the last 3 years. This lecture will give an insight into the medicinal chemistry story behind one clinical candidate and 2 marketed drugs. Crystal structures will be used to explain general principles behind designing for kinase inhibition, and some more advanced topics will be covered such as prodrugs, covalent inhibition and consideration of mutation status in drug discovery |
Thu 13 |
FS1 - Successful Completion of a Research Degree An hour devoted to a discussion of how to plan your time effectively on a day to day basis, how to produce a dissertation/thesis (from first year report to MPhil to PhD) and the essential requirements of an experimental section. FS2 - Dignity@Study The University of Cambridge is committed to protecting the dignity of staff, students, visitors to the University, and all members of the University community in their work and their interactions with others. The University expects all members of the University community to treat each other with respect, courtesy and consideration at all times. All members of the University community have the right to expect professional behaviour from others, and a corresponding responsibility to behave professionally towards others. Nick will explore what this means for graduate students in this Department with an opportunity to ask questions more informally. This is a compulsory session for 1st year postgraduates. |
Fri 14 |
Chemistry: DD9 Process Chemistry
Finished
Two complementary lecture from industry experts on process chemistry from GSK and Syngenta will share their experiences and challenges gathered over many years of experience. |
Mon 17 |
As the world population continues to grow, so does the need to increase global food production sustainably with limited resources. Agrochemicals, in the form of herbicides, fungicides and insecticides, provide an important tool for farmers to combat the weeds, fungi and insect pests that target their crops and help to ensure reliable yields and quality produce. Resistance, emerging pests, abiotic stress and regulatory pressure all drive an ongoing search for new and more innovative crop protection products. This lecture will outline the process used to discover new agrochemicals, from lead generation through to development. It will show the critical roles that chemistry, biology and human & environmental safety play, illustrated with a number of recent examples. |
Chemistry: Quantum Computing
Finished
Lecture 1 - Fundamentals of Quantum Computing A short summary of all the basic quantum computing knowledge needed to do quantum chemistry on a quantum computer. Lecture 2 - Encoding chemistry systems in quantum computers
Lecture 3 - Quantum algorithms for energy calculations
Lecture 4 - Advanced quantum chemistry quantum computing algorithms
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Tue 25 |
Chemistry: Quantum Computing
Finished
Lecture 1 - Fundamentals of Quantum Computing A short summary of all the basic quantum computing knowledge needed to do quantum chemistry on a quantum computer. Lecture 2 - Encoding chemistry systems in quantum computers
Lecture 3 - Quantum algorithms for energy calculations
Lecture 4 - Advanced quantum chemistry quantum computing algorithms
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Fri 28 |
Chemistry: Green Chemistry
Finished
This course will provide an overview of Sustainable Chemistry in the Pharmaceutical Industry: Motivation and Legislation It will cover the following in more detail;
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Chemistry: Green Chemistry
Finished
This course will provide an overview of Sustainable Chemistry in the Pharmaceutical Industry: Motivation and Legislation It will cover the following in more detail;
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March 2020
Mon 2 |
Chemistry: IS5 SciFinder and Reaxys
Finished
A ‘highly recommended’ optional course introducing electronic databases SciFinder and Reaxys presented by Professor Jonathan Goodman comprising of presentation followed by hands-on investigation. SciFinder https://www.cas.org/products/scifinder provides access to biochemical, chemical, chemical engineering, medical and other related information in journal and patent literature. Bibliographic, substance and reaction information is available. SciFinder includes references from more than 10,000 scientific journals and patent information from 63 patent issuing authorities. Sources include journals, patents, conference proceedings, dissertations, technical reports and books. It is one of the world’s largest collections of organic and inorganic substance information. It is possible to search by topic, author, company name, chemical structure, substructure, structure similarity and reaction. Personal registration is required for access to SciFinder on- and off-campus, please follow the instructions at: https://www-library.ch.cam.ac.uk/scifinder Reaxys combines the content of CrossFire Beilstein, Gmelin and the Patent Chemistry Database in one search. Validated reaction and substance data are integrated with synthesis planning. Data from all three sources are merged into one substance record. Unlimited access on-campus via the web: https://www.reaxys.com/. Off-campus access via Raven password. (Personal registration is not required for access). Please see the prerequisites. Please bring your own laptop for the practical element of the session. |
Chemistry: Quantum Computing
Finished
Lecture 1 - Fundamentals of Quantum Computing A short summary of all the basic quantum computing knowledge needed to do quantum chemistry on a quantum computer. Lecture 2 - Encoding chemistry systems in quantum computers
Lecture 3 - Quantum algorithms for energy calculations
Lecture 4 - Advanced quantum chemistry quantum computing algorithms
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Single Cell RNA Sequencing
Finished
The course will outlay bioinformatic analysis of cell populations from single-cell RNA including visualisation, clustering and functional analysis of genes. This will be using the programming language R and packages such as Seurat. Participants are encouraged to bring their own laptop to follow along. Lesson 1
Lesson 2
Lesson 3
Lesson 4
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