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Advanced Computational Methods in Drug Discovery: AI and Physics Based simulations

Vak
2022-2023

Admission requirements

Master students of Bio-Pharmaceutical Sciences, Biomedical Sciences, Life Science & Technology, Molecular Science & Technology. Required background knowledge is thorough knowledge of protein structure and organic chemistry at the level of Molecular Chemistry (for MSc LST students) or Organic Chemistry 2 (4052ORGC2 for BSc MST or equivalent).

Description

Computational approaches are now an established part of modern drug discovery. In this course, we will cover some of the main advances in the field, such as (but not limited to) molecular dynamics, free energy perturbation, machine learning and de novo molecular generation. The student will learn about the theoretical background of these approaches in a series of lectures, after which they will put this knowledge into practice to ultimately design a potential new drug candidate of their own. This course aims to:

  • Introduce key advances in the field of computational drug discovery

  • Leave the student with a set of tools to independently perform computer aided drug discovery approaches

  • Aid in understanding relevant literature in the field of computational drug discovery

  • Introduce public databases and repositories relevant to computational drug discovery

  • Understand and explain limitations of computational drug discovery

Course objectives

At the end of this course the student is able to:

  • describe and explain key methods in computational drug discovery

  • describe and explain basic machine learning models

  • design experimental setups for using computational modelling in drug discovery

  • read scientific literature, and can critically reflect on the rational and experimental approach of the study as well as on the results presented and conclusions drawn by the authors.

  • present key research findings to peers.

  • report key research findings in a written report

Timetable

You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.

MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).

For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.

Mode of instruction

Lectures (hoorcolleges)
Tutorial (werkcollege)
Practical work (praktisch werk/practica)
Tutoring (studiebegeleiding)
Self-tuition (zelfstudie)

Assessment method

The course will be concluded with a written report and a presentation. The written report will be counting for 80% and the presentation for 20% of the final grade for the course.

Reading list

Will be announced during the course.

Registration

From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.

Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.

Extensive FAQ's on MyStudymap can be found here.

Contact

Coordinator: Willem Jespers (e-mail: w.jespers@lacdr.leidenuniv.nl).

Remarks

This information is without prejudice. Alterations can be made for next year.