e-learning

Data management in Medicinal Chemistry

Abstract

The development of medicinal chemistry is advancing very rapidly. Big pharmaceutical companies, research institutes and universities are working on ground-breaking solutions to help patients combat all kinds of diseases. During that development process, tons of data are generated – not only from the lab environment but also from clinical trials. Given that the discovery of more potent, safer and cheaper drugs is the ultimate goal of all research bodies, we should all focus on making the data we gather FAIR: Findable, Accessible, Interoperable, and Reusable to push the boundaries of drug development even further.

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

  • Why does medicinal chemistry research produce so much data?
  • How can big data be useful for medicinal chemists?
  • What are the current publicly available databases that can be used for drug discovery and development?
  • How to use Galaxy for data management and analysis of chemical data?

Learning Objectives

  • Learn some terminology from the field of medicinal chemistry
  • Understand the idea of data-driven medicinal chemistry
  • Explore the databases used for drug discovery and development
  • Use Galaxy tools for data management, format conversion and simple analyses

Licence: Creative Commons Attribution 4.0 International

Keywords: FAIR Data, Workflows, and Research, computational-chemistry, data-management, fair, medicinal-chemistry

Target audience: Students

Resource type: e-learning

Version: 0

Status: Active

Prerequisites:

  • FAIR data management solutions
  • FAIR in a nutshell

Learning objectives:

  • Learn some terminology from the field of medicinal chemistry
  • Understand the idea of data-driven medicinal chemistry
  • Explore the databases used for drug discovery and development
  • Use Galaxy tools for data management, format conversion and simple analyses

Date modified: 2024-11-21

Date published: 2024-11-21

Authors: Julia Jakiela, Katarzyna Kamieniecka, Krzysztof Poterlowicz

Scientific topics: Computational chemistry

External resources:

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