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Imagedds: A Multi-Modal Deep Learning Model for Drug Synergy Prediction Using Image and Graph Representation Learning

https://doi.org/10.2139/ssrn.5152273
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Systems approaches and algorithms for discovery of combinatorial therapies
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Structure-activity relationships of Wee1 inhibitors: A review
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Learning from the global view: Supervised contrastive learning of multimodal representation
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What is synergy?
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Protein kinase inhibitors: contributions from structure to clinical compounds
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AttenSyn: An Attention-Based Deep Graph Neural Network for Anticancer Synergistic Drug Combination Prediction
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Analyzing Learned Molecular Representations for Property Prediction
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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
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Combinatorial drug therapy for cancer in the post-genomic era
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DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations
inferredcites →https://doi.org/10.1093/bib/bbab569openalex-snapshot
Multimodal deep learning for biomedical data fusion: a review
inferredcites →https://doi.org/10.1093/bib/bbad404openalex-snapshot
Chemical structure-aware molecular image representation learning
inferredcites →https://doi.org/10.1093/bib/bbw104openalex-snapshot
Predictive approaches for drug combination discovery in cancer
inferredcites →https://doi.org/10.1093/bioinformatics/btaa287openalex-snapshot
DTF: Deep Tensor Factorization for predicting anticancer drug synergy
inferredcites →https://doi.org/10.1093/bioinformatics/btx806openalex-snapshot
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
inferredcites →https://doi.org/10.1093/bioinformatics/bty294openalex-snapshot
Modeling polypharmacy side effects with graph convolutional networks
inferredcites →https://doi.org/10.1093/jamia/ocab162openalex-snapshot
GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction
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Prospective evaluation of weekly topotecan in recurrent platinum-resistant epithelial ovarian cancer
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Cross-Validatory Choice and Assessment of Statistical Predictions
inferredcites →https://doi.org/10.1158/0008-5472.can-09-1947openalex-snapshot
Drug Combination Studies and Their Synergy Quantification Using the Chou-Talalay Method
inferredcites →https://doi.org/10.1158/1535-7163.mct-15-0843openalex-snapshot
An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies
inferredcites →https://doi.org/10.1177/1947601912440575openalex-snapshot
Quantitative Methods for Assessing Drug Synergism
inferredcites →https://doi.org/10.1186/s12859-024-05765-yopenalex-snapshot
MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction
inferredcites →https://doi.org/10.1186/s13321-023-00690-3openalex-snapshot
DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks
inferredcites →https://doi.org/10.1371/journal.pcbi.1008653openalex-snapshot
TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations
inferredcites →https://doi.org/10.3390/biom14030253openalex-snapshot
SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy
inferredcites →https://doi.org/10.48550/arxiv.2005.00687openalex-snapshot
Open Graph Benchmark: Datasets for Machine Learning on Graphs
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