Inference of drug off-target effects on cellular signaling using interactome-based deep learning
Artikel i vetenskaplig tidskrift, 2024

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors’ activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

Medical informatics

Health informatics

Pharmacology

Bioinformatics

Health sciences

Biological sciences

Natural sciences

Författare

Nikolaos Meimetis

Massachusetts Institute of Technology

Douglas A. Lauffenburger

Massachusetts Institute of Technology

Avlant Nilsson

Massachusetts Institute of Technology

Karolinska Institutet

Chalmers, Life sciences, Systembiologi

iScience

25890042 (eISSN)

Vol. 27 4 109509

Maskininlärning av immunsystemet

Vetenskapsrådet (VR) (2019-06349), 2020-01-01 -- 2023-12-31.

Drivkrafter

Hållbar utveckling

Ämneskategorier

Biokemi och molekylärbiologi

Bioinformatik och systembiologi

DOI

10.1016/j.isci.2024.109509

PubMed

38591003

Mer information

Senast uppdaterat

2024-04-09