PHAR73002 15 Semester 2 Postgraduate Taught Level 7 Division of Pharmacy and Optometry 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 <p>In this unit, you will focus on the computer-aided elements of drug discovery and optimisation. You will learn about well-established computational methods ranging from chemical informatics through force-field-based simulation to quantum mechanical methods. You will also learn from practitioners of the latest tools applying AI and machine learning to drug discovery and optimisation. &nbsp;</p> <p>In this unit, you will focus on the computer-aided elements of drug discovery and optimisation. You will learn about well-established computational methods ranging from chemical informatics through force-field-based simulation to quantum mechanical methods. You will also learn from practitioners of the latest tools applying AI and machine learning to drug discovery and optimisation. &nbsp;</p> <p>The unit aims to: &nbsp;</p><p>- Provide students with an understanding of a range of computational drug design approaches including generative AI, quantum mechanics, docking/scoring, classical mechanics, molecular dynamics and cheminformatics. &nbsp;</p><p>- Provide students with an understanding of the domain of applicability of each of these techniques.&nbsp;</p> <p>Propose a sequence of computational methods to enable a drug discovery programme appreciating the considerations of time and cost constraints.</p><p>Appraise the theoretical foundations of a wide range of computational methods relevant to drug design</p> <p>Critically analyse data</p><p>Plan, conduct and report the screening of drug candidates</p><p>Frame problem that is amenable to their solution</p> <p>Apply a range of computational methods relevant to drug discovery</p><p>Apply available toolkits such as the QSAR Toolbox from ECHA</p><p>Perform virtual screening of compound libraries</p> <p>Demonstrate an ability to perform statistical analysis &nbsp;</p><p>Present results from a virtual screening campaign in an appropriate format for the audience</p><p>Manage time effectively</p><p>Use appropriate presentation software&nbsp;</p> <p>This module will be delivered in a face-to-face format over 3 weeks (block 3). Teaching material, including lectures that will be available online either prior to face-to-face (F2F) teaching sessions/tutorials or after live/F2F delivery. The face-to-face time will consist of a series of lectures and discussions in which core concepts (introduced through pre-reading) will be re-capped and any further development discussed, as well as supervised computer time during which practical software and programming problems will be explored. &nbsp;When possible, lectures will be recorded and distributed online. &nbsp;Alongside this, designated tutorials will be made available for students with academic staff as well as on-going support both online and F2F. Pair activities (e.g. 2D/3D screening) will be facilitated during F2F sessions to consider and reflect on a range of topics covered in the module.&nbsp;</p> 1 Written exam 40% 7 Oral assessment/presentation 60% <p>Exam feedback - On Canvas, within 15 working days. &nbsp;</p><p>&nbsp;</p><p>After each assessment feedback is returned, a drop-in session will also be held for students to get further feedback as required&nbsp;</p> MSc CADD Cancer Therapeutics MSc CADD Cancer Therapeutics PGDT Taught Component Mandatory N <p>A.R.Leach. Molecular Modelling: Principles and Applications, 2nd Ed. (Longman, 2001), ISBN-10: 0582382106&nbsp;</p> Lectures 10 Practical classes & workshops 68 82