Instead of the 'one drug, one gene, one disease' model of target-driven approaches, phenotype-driven drug discovery focuses on identifying compounds or, more broadly, perturbagens - combinations of therapeutic targets - that reverse disease phenotypes as measured by assays without predefined targets.
Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes.
PDGrapher points to parts of the cell that might be driving disease.
Next, it simulates what happens if these cellular parts were turned off or dialed down.
The AI model then offers an answer as to whether a diseased cell would happen if certain targets were “hit.”
By leveraging causal reasoning and representation learning on gene networks, PDGrapher identifies perturbagens necessary to achieve specific phenotypic changes.
This approach enables the direct prediction of therapeutic targets that can reverse disease phenotypes, bypassing the need for exhaustive response simulations across large perturbation libraries.
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