Work

Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition (Adv. Sci. 47/2024)

Year: 2024

Type: article

Abstract: Advancing Anticancer Drug Discovery In article number 2404085, Ludger A. Wessjohann, Gerd U. Balcke, and co-workers address the challenge of identifying the mode of action (MoA) of anti-cancer drugs by integrating metabolomics and machine learning. Analyzing 38 drugs in prostate cancer cells, the authors accurately predicted MoAs, including for novel compounds like natural products. The approach reveals intracellular mechanisms, is transferable across cancer types and offers new opportunities for optimizing combinatorial drug therapies. Cover image created by Dilara Balci, IPB Halle (Saale).

Source: Advanced Science

DOI: 10.1002/advs.202470288

Authors: Mohamad Saoud, Jan Grau, Robert Rennert, Thomas Mueller, Mohammad Yousefi, Mehdi D. Davari, Bettina Hause, René Csuk, Luay Rashan, Ivo Grosse, Alain Tissier, Ludger A. Wessjohann, Gerd U. Balcke

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $6,120

OpenAlex ID: W4405539257

FWCI:

Cites:

Cited by:

Related to: 10