Department of Chemistry course timetable
December 2019
Thu 12 
Spectroscopic methods in biochemistry and biophysics are powerful tools to characterise the chemical properties of samples in chemistry and biology, including molecules, macromolecules, living organisms, polymers and materials. Within the wide class of biophysical methods, infrared spectroscopy (IR) is a sensitive analytical labelfree tool able to identify the chemical composition and properties of a sample through its molecular vibrations, which produce a characteristic fingerprint spectrum. An infrared spectrum is commonly obtained by passing infrared radiation through a sample and determining what fraction of the incident radiation is absorbed at a particular energy. The energy at which any peak in an absorption spectrum appears corresponds to the frequency of a vibration of a part of a sample molecule. One of the great advantages of infrared spectroscopy is that virtually any sample in virtually any state may be studied, such as liquids, solutions, pastes, powders, films, fibres, gases and surfaces can all be examined. In this introductory course, the basic ideas and deﬁnitions associated with infrared spectroscopy will be described. First, the possible configurations of the spectrometers used to measure IR absorption will be discussed. Then, the vibrations of molecules, inorganic and organic chemical compounds, as well as large biomolecules will be introduced, as these are crucial to the interpretation of infrared spectra in every day experimental life. 
January 2020
Tue 14 
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:

This graduatelevel 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 realworld examples on how ML methods have transformed the way they perform research. 

Wed 15 
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. 
Drug discovery is a complex multidisciplinary process with chemistry as the core discipline. A small molecule New Chemical Entity (NCE) (80% of drugs marketed) has had its genesis in the mind of a chemist. A successful drug is not only biologically active (the easy bit), but is also therapeutically effective in the clinic – it has the correct pharmacokinetics, lack of toxicity, is stable and can be synthesised in bulk, selective and can be patented. Increasingly, it must act at a genetically defined subpopulation of patients. Medicinal chemists therefore work at the centre of a web of disciplines – biology, pharmacology, molecular biology, toxicology, materials science, intellectual property and medicine. This fascinating interplay of disciplines is the intellectual space within which a chemist has to make the key compound that will become an effective medicine. It happens rarely, despite enormous investment in time, money and effort. What factors make a program successful? I would like to briefly outline the process, but importantly to offer some key with examples of success 

Thu 16 
This graduatelevel 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 realworld examples on how ML methods have transformed the way they perform research. 
Fri 17 
Drug discovery is a complex multidisciplinary process with chemistry as the core discipline. A small molecule New Chemical Entity (NCE) (80% of drugs marketed) has had its genesis in the mind of a chemist. A successful drug is not only biologically active (the easy bit), but is also therapeutically effective in the clinic – it has the correct pharmacokinetics, lack of toxicity, is stable and can be synthesised in bulk, selective and can be patented. Increasingly, it must act at a genetically defined subpopulation of patients. Medicinal chemists therefore work at the centre of a web of disciplines – biology, pharmacology, molecular biology, toxicology, materials science, intellectual property and medicine. This fascinating interplay of disciplines is the intellectual space within which a chemist has to make the key compound that will become an effective medicine. It happens rarely, despite enormous investment in time, money and effort. What factors make a program successful? I would like to briefly outline the process, but importantly to offer some key with examples of success 
Mon 20 
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 21 
This graduatelevel 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 realworld examples on how ML methods have transformed the way they perform research. 
Wed 22 
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. 
This graduatelevel 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 realworld examples on how ML methods have transformed the way they perform research. 

Thu 23 
This graduatelevel 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 realworld examples on how ML methods have transformed the way they perform research. 
Fri 24 
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 nontraditional modalities. This lecture will also serve as an introduction to the medicinal chemistry game. 
Mon 27 
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 
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 graduatelevel 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 realworld examples on how ML methods have transformed the way they perform research. 

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. 

Thu 30 
This graduatelevel 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 realworld 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 graduatelevel 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 realworld 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 designmaketestanalysis 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 DD1DD4. 

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:
