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Drug Combinations

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CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Briefings in bioinformatics
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug sy...

Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism.

Briefings in bioinformatics
Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did...

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations.

Briefings in bioinformatics
Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of po...

Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected ph...

A review of machine learning approaches for drug synergy prediction in cancer.

Briefings in bioinformatics
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number ...

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

Molecular cancer research : MCR
UNLABELLED: Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is...

An enhanced cascade-based deep forest model for drug combination prediction.

Briefings in bioinformatics
Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artif...

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Briefings in bioinformatics
Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds i...

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

Briefings in bioinformatics
MOTIVATION: Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so huge that it is hard to screen synergistic drug combinations through wet-lab experime...

Machine learning methods, databases and tools for drug combination prediction.

Briefings in bioinformatics
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In ...