Drug Discovery Course
What will I learn?
Ggula ebyama ebikwata ku kuzula eddagala eriggya n'ekibinja kyaffe ekijjudde ebikulu ebikwatagana n'abakugu mu bya Biological Sciences. Yinga mu by'emikemeko gy'eddagala, ng'otunulira enkozesa y'ebikemeko n'enkolagana y'ebintu ebikola eddagala n'obukodyo bwabyo. Yiga ebikulu mu nkola y'okuzuula eddagala, okuva ku kulonda ebyo bye twagala okutuukira ddala ku kulongoosa ebintu ebikola eddagala. Kulakulanya obukugu bwo mu nkola z'okunoonyereza ku data, nga mw'otwalidde n'enkola za machine learning. Funa obumanyirivu mu bikozesebwa bya bioinformatics ne mu nkola y'okukola eddagala ku kompyuta (in silico drug design), ng'otadde essira ku kulagula ebya ADMET n'enkolagana y'ebintu. Yongeza obukugu bwo mu by'eddagala n'obutwa, okukakasa okutegeera okw'amaanyi ku bya pharmacokinetics ne pharmacodynamics. Wegatte ku ffe okukyusa omulimu gwo n'okumanya ebintu ebiggymu omugaso ate era eby'omutindo ogwa waggulu.
Develop skills
Strengthen the development of the practical skills listed below
Yiga enkozesa y'ebikemeko okukola eddagala eriggya.
Noonyereza ku SAR okukulaakulanya ebintu eby'omugaso.
Longoosa ebintu ebikola eddagala okwongera ku maanyi g'eddagala.
Kozesa bioinformatics okukola ebifananyi bya protein ebitaliimu nsobi.
Kwasiza machine learning okwanguya enkozesa y'okuzuula eddagala.
Suggested summary
Workload: between 4 and 360 hours
Before starting, you can change the chapters and the workload.
- Choose which chapter to start with
- Add or remove chapters
- Increase or decrease the course workload
Examples of chapters you can add
You will be able to generate more chapters like the examples below
This is a free course, focused on personal and professional development. It is not equivalent to a technical, undergraduate, or postgraduate course, but offers practical and relevant knowledge for your professional journey.