"In a summary of the study, which was partially federally funded, the authors explain that usual drug discovery approaches tackle one protein at a time, and work in cases such as kinase inhibitors -- drugs that prevent cancer cells from expanding by blocking certain proteins -- but can fall short when the disease involves interactions between multiple signaling pathways and genes."
""Traditional drug discovery resembles tasting hundreds of prepared dishes to find one that happens to taste perfect," said study senior author Marinka Zitnik in the summary. "PDGrapher works like a master chef who understands what they want the dish to be and exactly how to combine ingredients to achieve the desired flavor.""
"According to Harvard, the model can analyze the connections between genes, proteins, and signaling pathways inside cells to identify the best combination of therapies that would effectively restore healthy cell behavior. This could enable new treatments for conditions that have previously been unable to be found via traditional methods."
PDGrapher is an AI model that analyzes connections between genes, proteins, and signaling pathways inside cells to identify optimal therapy combinations that restore healthy cell behavior. The model learned from datasets of diseased cells before and after treatment to determine which genes drive transitions toward health. Traditional single-protein targeting approaches can fail when diseases involve multiple interacting pathways. PDGrapher evaluates multi-pathway interactions and was evaluated across multiple cancer datasets. The approach can enable combination treatments for conditions that elude single-target methods and could accelerate discovery of therapies for complex cellular disorders.
Read at ZDNET
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