All Department of Chemistry courses

Science is a strikingly successful and powerful feature of contemporary human cultures: it has transformed lives, enabled great technological feats and often revealed the world to be a much stranger place than appearances suggest. But what is science, really, and how and why has it been so successful?

An 8 week Improv Theatre Course Improv teaches excellent skills for scientists! It will boost your confidence, teach you to be spontaneous and overcome the fear of failure. It will work wonders for your public speaking, communication and presentation skills.

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.

Once you book this course, you will need to register for each session via Zoom.

The Departmental Advanced Safety Training covers basic induction training in how to work safely, including emergency arrangements for fire and evacuation, first aid and incidents including flood and gas leak. By attending, you are made aware of the Department’s Health and Safety Policy and your responsibilities under health and safety law. You will be introduced to the process required to prepare a risk assessment with standard operating procedure (SOP) or method statement, how to select the correct type of protective equipment (PPE) and why it needs to be worn, and reminded of the importance of good house keeping for reducing the likelihood of there being an incident. The hazards associated with display screen equipment (DSE) and manual handling are identified and the need to control them by a suitable and sufficient assessment of the risk is explained. Electrical safety and the requirement for annual Portable Appliance Testing (PAT) is made clear.

• Please note you will find this training on Moodle.

Part of Induction Week

Advanced induction training for experimentalists introduces some of the department’s special chemical hazards including explosives, hydrogen fluoride and cyanide, and restricted chemicals, and illustrates the consequences of incorrect waste disposal. Experimentalists are made aware of the biological hazards in the department and how these are controlled with a suitable risk assessment, safety cabinets and the need for the appropriate inactivation method to be applied. Attendees are alerted to the hazards and damage caused by non-ionising radiation, glassware and sharps, oil baths and lifting equipment. The induction concludes by directing the experimentalist to compulsory University-provided specialist training courses, the requirement for fire awareness training and sources of Health and Safety information.

• This training is will be available on Moodle.

This course will cover safe storage and use of cryogens, safe use and stores of compressed gas, and aspects of oxygen depletion with respect to the above.

• This training will be available on Moodle.

This course will focus on recent progress in the application of kernel-based methods, Random Forests and Deep Neural Networks to modelling in chemistry. The material will build on the content of the core Informatics course and introduce new descriptors, advanced modelling techniques and example applications drawn from the current literature. Lectures will be interactive, with students working through computational exercises during class sessions.

An applied introduction to probabilistic modelling, machine learning and artificial intelligence-based approaches for students with little or no background in theory and modelling. The course will be taught through a series of case studies from the current literature in which modelling approaches have been applied to large datasets from chemistry and biochemistry. Data and code will be made available to students and discussed in class. Students will become familiar with python based tools that implement the models though practical sessions and group based assignments.

This course will introduce students to the central question of how to encode molecules and molecular properties in a computational model. Building on the compulsory informatics course (see previous table entry), it will focus on reactivity parameterisation and prediction. The basics of DFT calculations will be introduced, together with how DFT can be used to model reactions (including flaws, assumptions, drawbacks etc). Lecture based format will be complemented by practical sessions in setting up different DFT-based calculations.

There are 8 sessions in total DD1 to DD8 starting from 18th May and ending 10th June. The sessions are listed below:

DD1: Introduction to Drug Discovery and path to clinic Bobby Glen (UoC) 18th May, 10:00 - 12:00

SESSION CANCELLED DD2: Pharmacology + Biochemical and Biophysical methods Chris Stubbs (AZ) 20th May, 10:00 - 12:00

DD3: Structural Biology Gavin Collie (AZ) 25th May, 10:00 - 12:00

DD4: Hit generation methods and tactics Ben Whitehurst (AZ) 27th May, 10:00 - 12:00

DD5: Potency & thermodynamics Steve Atkinson (AZ) 1st June, 10:00 - 12:00

DD6: Computational methods (Session 1) - Modelling/MD/potency prediction/ML/AI Kathryn Giblin (AZ) 3th June, 10:00 - 12:00

DD7: Computational methods (Session 2) - Modelling/MD/potency prediction/ML/AI Bobby Glen (UoC) 8th June, 10:00 - 12:00

DD8: Impact of structures and physchem on DMPK/safety Jen Nelson (AZ) 10th June, 10:00 - 12:00

'Enhance focus, reduce stress, use time more wisely and be more productive.

Learn to:

• Establish a method that works for you to enhance focus for the most important work (Deep Work)
• Reduce distraction and prioritise more effectively
• Establish 1 daily high quality mini break, to relieve stress, reduce self criticism and strengthen resilience
• Create the space to recognise your achievements each day - increase self awareness and confidence
• Combining proven neuroscience & mindfulness based techniques into useful daily habits.

In these sessions, Dr. Mukund S. Chorghade will discuss the pivotal role played by Process Chemistry / Route Selection in the progress of a chemical entity from conception to commercialization.

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These lectures will provide an introduction to the field with relevant background and theory, a survey of main strategies that have been used and are most widely practised and finally will cover current challenges and latest approaches in the area.

In this course we will give a brief introduction to the theory and simulation of molecules and materials. The focus will be on explaining at an introductory level the types of problems and properties that can be tackled with current techniques in theoretical chemistry. Limitations of current methods and future perspectives of where the field is heading and its intersection with modern experimental methods will also be discussed.

In these introductory lectures, you will learn how machine learning inspired methods have been making inroads into molecular modelling, particularly first principles modelling. The focus will be on descriptors and representations of atomic geometry and modelling potential energy surfaces.

The course will be a brief overview of polymer chemistry, covering a range of synthetic methods and interests in the context of drug delivery.

These lectures seek to provide an overarching vision of chemical synthesis methodology using machinery as enabling tools. They will highlight current capabilities and limitations in this highly digitally connected world and suggest where new opportunities may arise in the future, going well beyond our present levels of innovation and automation.

In order to use machine learning methods on molecular data, it is necessary to express molecular structures in a form which can be used as the input. This workshop will outline ways in which this challenge has been addressed, including the InChI, SMILES, fingerprints and other ways of expressing molecules as text strings. The strengths and weaknesses of the various approaches makes them suitable for different applications. What will be most appropriate for the molecular problems you are tackling?

Course provider: Timur Madzhidov

Course description: This is an advanced workshop providing a hands-on opportunity to work on several case studies in teams during the workshop. Several applications of classical ML and deep learning approaches in chemistry will be reviewed. As part of the tasks assigned to groups, the fundamentals such as data acquisition, preparation and modelling will be included.

This complimentary hands-on workshop is offered to PhD students and researchers at University of Cambridge who want to learn more about design of experiments (DOE) and data analysis. DOE skills are highly demanded by industry and still under-represented in many university curricula. Design of experiments is a practical and ubiquitous approach for exploring multifactor opportunity spaces, and JMP offers world-class capabilities for design and analysis in a form you can easily use without any programming. To properly uncover how inputs (factors) jointly affect the outputs (responses), DOE is the most efficient and effective way – and the only predictable way – of learning. Unlike the analysis of existing data, designed experiments can tell you about cause and effect, drive innovation and test opportunities by exploring new factor spaces. In addition to classical DOE designs, JMP also offers an innovative custom design capability that tailors your design to answer specific questions without wasting precious resources. Once the data has been collected, JMP streamlines the analysis and model building so you can easily see the pattern of response, identify active factors and optimize responses.

In this course you will learn to understand why to consider DOE analyze experiments with a single categorical factor using analysis of variance (ANOVA) analyze experiments with a single continuous factor using regression analysis understand the difference between classical and optimal designs design, analyze and interpret screening experiments incl. Definitive Screening Design design, analyze and interpret experiments in response surface methodology augment designs for sequential experimentation apply robust optimization evaluate and compare designs understand advanced features like blocking, split-plot experiments and covariates

The format of this course will be a mix of concept presentations, live demos and hands-on exercises. Most examples are inspired by chemistry and biotech, but can be easily transferred to other fields like materials science, agri-food science or engineering. Attendees should have access to JMP Pro (pre-installed). JMP Pro 17 is available for all attendees from University of Cambridge for both Windows and Mac. No prior knowledge required. All content and demos will be shared with the participants.

The course will be delivered by Lucy Colwell

This course will be delivered in person or via Zoom.

You will be informed closer to the date

This course will focus on recent progress in the application of kernel-based methods, Random Forests and Deep Neural Networks to modelling in chemistry. The material will build on the content of the core Informatics course and introduce new descriptors, advanced modelling techniques and example applications drawn from the current literature. Lectures will be interactive, with students working through computational exercises during class sessions.

The course will be delivered by Lucy Colwell

This course will be delivered in person or via Zoom.

You will be informed closer to the date

An applied introduction to probabilistic modelling, machine learning and artificial intelligence-based approaches for students with little or no background in theory and modelling. The course will be taught through a series of case studies from the current literature in which modelling approaches have been applied to large datasets from chemistry and biochemistry. Data and code will be made available to students and discussed in class. Students will become familiar with python based tools that implement the models though practical sessions and group based assignments.

The course will be delivered by Lucy Colwell This course will be delivered in person or via Zoom. You will be informed closer to the date

This course will introduce students to the central question of how to encode molecules and molecular properties in a computational model. Building on the compulsory informatics course (see previous table entry), it will focus on reactivity parameterisation and prediction. The basics of DFT calculations will be introduced, together with how DFT can be used to model reactions (including flaws, assumptions, drawbacks etc). Lecture based format will be complemented by practical sessions in setting up different DFT-based calculations.