"Unlike traditional approaches that typically test one protein target or drug at a time in hopes of identifying an effective treatment, the new model, called PDGrapher and available for free, focuses on multiple drivers of disease and identifies the genes most likely to revert diseased cells back to healthy function. The tool also identifies the best single or combined targets for treatments that correct the disease process. The work, described Tuesday in Nature Biomedical Engineering, was supported in part by federal funding."
"By zeroing in on the targets most likely to reverse disease, the new approach could speed up drug discovery and design and unlock therapies for conditions that have long eluded traditional methods, the researchers noted. "Traditional drug discovery resembles tasting hundreds of prepared dishes to find one that happens to taste perfect," said study senior author Marinka Zitnik, associate professor of biomedical informatics in the Blavatnik Institute at HMS. "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.""
PDGrapher is an artificial intelligence model that identifies treatments capable of reversing disease states in cells. The model evaluates multiple disease drivers and prioritizes genes most likely to revert diseased cells to healthy function. PDGrapher recommends optimal single targets or combinations for interventions that correct underlying disease processes. The approach addresses conditions driven by interactions across signaling pathways and genes, overcoming limits of single-protein targeting paradigms. The tool is available for free, has received federal support, is described in Nature Biomedical Engineering, and is being applied to neurodegenerative disorders such as Parkinson's and Alzheimer's disease.
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