What is it about?
Conventional drug discovery methods primarily focus on how drugs bind to target molecules, overlooking complex interactions between genes and chemicals. This can limit their effectiveness. This paper proposes a method to do it with “complex” trial-and-errors.
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Why is it important?
By considering the phenomenon only, it analyzes data from end-to-end. It side-steps from complications of binding target molecules in the complex environment of the cells, with all kinds of proteins and other molecules.
Perspectives
This paper introduces a deep learning model that learns directly from textual descriptions of drugs and their efficacy against viruses. This model captures intricate relationships without relying on detailed binding information. By leveraging the diversity of chemical structures in drugs, it performs even with limited data availability. This approach opens up new possibilities for drug discovery, particularly when our understanding of molecular mechanisms is incomplete.
Billy Yu
IPM
Read the Original
This page is a summary of: A phenotypic drug discovery approach by latent interaction in deep learning, Royal Society Open Science, October 2024, Royal Society Publishing,
DOI: 10.1098/rsos.240720.
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