Molecular cocktails

Task

The task is to predict synergistic effects (properties) of drug combinations (cocktails).

Antiviral drugs are typically administered as cocktails so it is important to model synergistic effects of drug combinations. Given the combinatorial nature of cocktails, it is not practical to screen empirically all possible combinations, increasing importance of in-silico modeling.

Data

For all the datasets, a training instance is represented by a drug combinations (A,B) and their activity measurement. Each drug is represented by its structures (SMILES string).
 

  1. NCI cancer drug combination dataset: combination of FDA-approved cancer drugs performed in killing cancer cells.
     

  2. DrugComboDB: database of drug combinations extracted from various sources.

Model

A graph convolutional network with inter-molecule attention on the NCI dataset to predict synergistic effects.

References

[1] Holbeck, Susan L., et al. "The National Cancer Institute ALMANAC: a comprehensive screening resource for the detection of anticancer drug pairs with enhanced therapeutic activity." Cancer research 77.13 (2017): 3564-3576. (https://cancerres.aacrjournals.org/content/77/13/3564.long) - NCI cancer drug combination: 


[2] Jin, Wengong, et al. "Predicting organic reaction outcomes with weisfeiler-lehman network." Advances in Neural Information Processing Systems. 2017. (https://papers.nips.cc/paper/6854-predicting-organic-reaction-outcomes-with-weisfeiler-lehman-network.pdf) - Graph convolution with inter-molecule attention

[3] Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network: https://github.com/wengong-jin/nips17-rexgen

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