MOOMIN: Deep Molecular Omics Network for Anti-Cancer Drug Combination Therapy

by   Benedek Rozemberczki, et al.

We propose the molecular omics network (MOOMIN) a multimodal graph neural network that can predict the synergistic effect of drug combinations for cancer treatment. Our model captures the representation based on the context of drugs at multiple scales based on a drug-protein interaction network and metadata. Structural properties of the compounds and proteins are encoded to create vertex features for a message-passing scheme that operates on the bipartite interaction graph. Propagated messages form multi-resolution drug representations which we utilized to create drug pair descriptors. By conditioning the drug combination representations on the cancer cell type we define a synergy scoring function that can inductively score unseen pairs of drugs. Experimental results on the synergy scoring task demonstrate that MOOMIN outperforms state-of-the-art graph fingerprinting, proximity preserving node embedding, and existing deep learning approaches. Further results establish that the predictive performance of our model is robust to hyperparameter changes. We demonstrate that the model makes high-quality predictions over a wide range of cancer cell line tissues, out-of-sample predictions can be validated with external synergy databases, and that the proposed model is data-efficient at learning.



There are no comments yet.


page 1

page 2

page 3

page 4


Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models

Drug resistance is still a major challenge in cancer therapy. Drug combi...

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

Drug combination therapy has become a increasingly promising method in t...

A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning

By combining various cancer cell line (CCL) drug screening panels, the s...

Interpretable Drug Synergy Prediction with Graph Neural Networks for Human-AI Collaboration in Healthcare

We investigate molecular mechanisms of resistant or sensitive response o...

Mass-preserving approximation of a chemotaxis multi-domain transmission model for microfluidic chips

The present work was inspired by the recent developments in laboratory e...

SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations

Medication recommendation is an essential task of AI for healthcare. Exi...

Attentional Multilabel Learning over Graphs - A message passing approach

We address a largely open problem of multilabel classification over grap...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.