Isuru Herath

2 months ago | biorxiv.org | Guadalupe Gonzalez |Guadalupe E González |Xiang Lin |Isuru Herath |Kirill Veselkov

AbstractPhenotype-driven approaches identify disease-counteracting compounds by analyzing the phenotypic signatures that distinguish diseased from healthy states. These approaches can guide the discovery of targeted perturbations, including small-molecule drugs and genetic interventions, that modulate disease phenotypes toward healthier states.

Oct 7, 2024 | biorxiv.org | Guadalupe Gonzalez |Guadalupe E González |Xiang Lin |Isuru Herath |Kirill Veselkov

AbstractAs an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network (GNN) designed to predict combinatorial perturbagens - sets of therapeutic targets - capable of reversing disease effects.

Jan 8, 2024 | biorxiv.org | Guadalupe Gonzalez |Guadalupe E González |Isuru Herath |Kirill Veselkov |Michael Bronstein

AbstractAs an alternative to target-driven drug discovery, phenotype-driven approaches identify compounds that counteract the overall disease effects by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens - sets of therapeutic targets - capable of reversing disease effects.

Jan 3, 2024 | biorxiv.org | Guadalupe Gonzalez |Guadalupe E González |Kirill Veselkov |Michael Bronstein |Isuru Herath

AbstractPhenotype-driven drug discovery, as an emerging alternative to target-driven strategies, identifies compounds that counteract the overall effects of diseases by analyzing phenotypic signatures. Our study introduces a novel approach to this field, aiming to expand the search space for new therapeutic agents. We introduce PDGrapher, a causally-inspired graph neural network model designed to predict arbitrary perturbagens - a set of therapeutic targets - capable of reversing disease effects.