This repository contains eight sessions delivered entirely through Jupyter notebooks, introducing fundamental skills in computational drug discovery. The workflow proceeds from classical pharmacology concepts through cheminformatics, machine learning, virtual screening, and finally molecular docking using AutoDock Vina.
The course content is aimed at undergraduate students but it is expected that users have grasped some fundamental Python skills. The content aims to build on the the Data-Driven Chemistry course prepared by the University of Edinburgh’s School of Chemistry while focusing on Drug Discovery.
The sessions were prepared by a summer student and Manchester Metropolitan University and has received funding from the AI for Chemistry: AIchemy Hub (EPSRC grant EP/Y028775/1 and EP/Y028759/1).
The work is ongoing, especially the final sessions on BioActivity and AutoDock Vina. The intention is to develop the content into a computational chemistry course on Drug Discovery that can be delivered at Manchester Met. and beyond. Therefore, contributions to the learning resource are welcome.
- Mr Zhaohui Jiang
- Dr Alex Aziz (a.aziz@mmu.ac.uk)
These materials are made freely available, and are licensed under a CC-BY 4.0 license.