Department of Chemistry course timetable

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

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

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.

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.

This course will introduce the new generation of molecules, including novel peptides, oligonucleotides, hybrids, and molecular conjugates, that are enabling novel strategies to address challenging targets and biological processes. This workshop will review these next generation therapeutics, and will highlight progress in this area towards a range of novel drug candidates.

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.

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.

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.

This course explores how to write a paper and improve your writing style in Chemistry papers. The course will be particularly useful for those working on their first year report, MPhil thesis or an article.

This event is in-person only.

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.

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.

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?