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Department of Chemistry

Department of Chemistry course timetable

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Wed 22 Jan 2020 – Mon 24 Feb 2020

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January 2020

Wed 22
Chemistry: SC1-10 Statistics for Chemists (4 of 8) [Places] 10:00 - 12:00 G30

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: Machine Learning in Chemistry 101 new (4 of 9) [Full] 13:00 - 14:00 Unilever Lecture Theatre

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.

Thu 23

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.

Chemistry: Machine Learning in Chemistry 101 new (5 of 9) [Full] 13:00 - 16:00 Unilever Lecture Theatre

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 24
Chemistry: DD3 Modern Tactics to Optimise Potency [Places] 14:00 - 15:00 Unilever Lecture Theatre

When you have 1000s of possible compounds you could make from any one start point what do you make first? This lecture will cover some general basic principles on designing more potent molecules, as well as some practical tips on how to run an optimization program and how to focus synthetic efforts. Binding modalities (reversible, covalent) will be briefly covered, as well as some newer non-traditional modalities. This lecture will also serve as an introduction to the medicinal chemistry game.

Mon 27
Chemistry: SC1-10 Statistics for Chemists (5 of 8) [Places] 10:00 - 12:00 G30

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 28
Chemistry: FS12 Managing your Supervisor Relationship [Places] 09:30 - 13:00 Chemistry of Health

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

Chemistry: Machine Learning in Chemistry 101 new (6 of 9) [Full] 13:00 - 14:00 Unilever Lecture Theatre

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.

Wed 29
Chemistry: SC1-10 Statistics for Chemists (6 of 8) [Places] 10:00 - 12:00 G30

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 [Places] 14:00 - 15:00 Unilever Lecture Theatre

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.

Thu 30
Chemistry: Machine Learning in Chemistry 101 new (7 of 9) [Full] 13:00 - 16:00 Unilever Lecture Theatre

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 [Places] 10:15 - 10:45 Library

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
Chemistry: SC1-10 Statistics for Chemists (7 of 8) [Places] 10:00 - 12:00 G30

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
Chemistry: Machine Learning in Chemistry 101 new (8 of 9) [Full] 13:00 - 14:00 Unilever Lecture Theatre

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.

Chemistry: DD5 Medicinal Chemistry Game Workshop [Places] 14:00 - 17:00 Todd-Hamied

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.

Wed 5
Chemistry: SC1-10 Statistics for Chemists (8 of 8) [Places] 10:00 - 12:00 G30

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
Chemistry: IS4 Research Data Management [Places] 10:00 - 12:00 Todd-Hamied

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:

  • Research Data Management (RDM) - what it is and what problems can occur with managing and sharing your data.
  • Data backup and file sharing - possible consequences of not backing up your data, strategies for backing up your data and sharing your data safely.
  • Data organisation - how to organise your files and folders, what is best practice.
  • Data sharing - obstacles to sharing your data, benefits and importance of sharing your data, the funder policy landscape, resources available in the University to help you share your data.
  • Data management planning - creating a roadmap for how not to get lost in your data!

Lunch and refreshments are included for this course

Chemistry: Machine Learning in Chemistry 101 new (9 of 9) [Full] 13:00 - 14:00 Unilever Lecture Theatre

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 7
Chemistry: DD6 Toxicity and Drug Safety [Places] 14:00 - 15:00 Unilever Lecture Theatre

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
Chemistry: Quantum Computing new (1 of 4) [Places] 14:00 - 15:00 Todd-Hamied

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

  • Second quantization
  • Jordan-Wigner and Bravyi-Kitaev transforms
  • Molecular orbital encoding
  • State Preparation

Lecture 3 - Quantum algorithms for energy calculations

  • NISQ: Variational quantum algorithms
  • Future: Phase Estimation algorithms

Lecture 4 - Advanced quantum chemistry quantum computing algorithms

  • Excited Algorithms: QSE, Constrained Minimisation, etc
  • Special Ansatz using symmetry
  • Imaginary time evolution
  • TBA
Wed 12
Chemistry: DD7 Kinase Inhibitor Case Studies [Places] 14:00 - 15:00 Unilever Lecture Theatre

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

Fri 14
Chemistry: DD9 Process Chemistry [Places] 13:00 - 15:30 Todd-Hamied

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
Chemistry: DD8 Agrochemical Discovery [Places] 11:00 - 12:00 Todd-Hamied

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 new (2 of 4) [Places] 14:00 - 15:00 Todd-Hamied

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

  • Second quantization
  • Jordan-Wigner and Bravyi-Kitaev transforms
  • Molecular orbital encoding
  • State Preparation

Lecture 3 - Quantum algorithms for energy calculations

  • NISQ: Variational quantum algorithms
  • Future: Phase Estimation algorithms

Lecture 4 - Advanced quantum chemistry quantum computing algorithms

  • Excited Algorithms: QSE, Constrained Minimisation, etc
  • Special Ansatz using symmetry
  • Imaginary time evolution
  • TBA
Mon 24
Chemistry: Quantum Computing new (3 of 4) [Places] 14:00 - 15:00 Todd-Hamied

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

  • Second quantization
  • Jordan-Wigner and Bravyi-Kitaev transforms
  • Molecular orbital encoding
  • State Preparation

Lecture 3 - Quantum algorithms for energy calculations

  • NISQ: Variational quantum algorithms
  • Future: Phase Estimation algorithms

Lecture 4 - Advanced quantum chemistry quantum computing algorithms

  • Excited Algorithms: QSE, Constrained Minimisation, etc
  • Special Ansatz using symmetry
  • Imaginary time evolution
  • TBA