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A Deep Learning Framework for Prediction of the Mechanism of Action

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Jingyuan Dai, Shahram Latifi

Jingyuan Dai and Shahram Latifi. A Deep Learning Framework for Prediction of the Mechanism of Action. International Journal of Computer Applications 183(12):1-7, June 2021. BibTeX

	author = {Jingyuan Dai and Shahram Latifi},
	title = {A Deep Learning Framework for Prediction of the Mechanism of Action},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {12},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {1-7},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2021921383},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper aims to apply deep learning algorithms to advance a new drug’s mechanism of action (MoA) prediction. Since one drug can have one or more MoAs, algorithms must be developed to perform multi-label classification problems. This paper puts forward a deep learning framework, MoA Net, which ensembles one residual network and five convolutional neural networks to predict MoA targets. To find optimal parameter sets, the authors implements Bayesian tuning techniques on each sub network of MoA Net. The study uses logarithmic loss function to evaluate the model’s performance. Results show successful MoA target prediction in the dataset provided by the LISH and LINCS Connectivity Map.


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Convolutional Neural Nets, Genetic Expression, Drugs, Mechanism of Actions, Residual Networks