RT Journal Article
SR Electronic
T1 Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.01.03.573985
DO 10.1101/2024.01.03.573985
A1 Gonzalez, Guadalupe
A1 Herath, Isuru
A1 Veselkov, Kirill
A1 Bronstein, Michael
A1 Zitnik, Marinka
YR 2024
UL http://biorxiv.org/content/early/2024/01/08/2024.01.03.573985.abstract
AB As 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. Unlike existing methods that learn responses to perturbations, PDGrapher solves the inverse problem, which is to infer the perturbagens necessary to achieve a specific response – i.e., directly predicting perturbagens by learning which perturbations elicit a desired response. Experiments across eight datasets of genetic and chemical perturbations show that PDGrapher successfully predicted effective perturbagens in up to 9% additional test samples and ranked therapeutic targets up to 35% higher than competing methods. A key innovation of PDGrapher is its direct prediction capability, which contrasts with the indirect, computationally intensive models traditionally used in phenotype-driven drug discovery that only predict changes in phenotypes due to perturbations. The direct approach enables PDGrapher to train up to 30 times faster, representing a significant leap in efficiency. Our results suggest that PDGrapher can advance phenotype-driven drug discovery, offering a fast and comprehensive approach to identifying therapeutically useful perturbations.Competing Interest StatementG.G. is currently employed by Genentech, Inc. and F. Hoffmann-La Roche Ltd. I.H. is currently employed by Merck & Co., Inc.