Department of Chemistry course timetable

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?

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.

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.

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.

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.