CFP last date
20 March 2024
Reseach Article

A Deep Learning Framework for Prediction of the Mechanism of Action

by Jingyuan Dai, Shahram Latifi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 12
Year of Publication: 2021
Authors: Jingyuan Dai, Shahram Latifi

Jingyuan Dai, Shahram Latifi . A Deep Learning Framework for Prediction of the Mechanism of Action. International Journal of Computer Applications. 183, 12 ( Jun 2021), 1-7. DOI=10.5120/ijca2021921383

@article{ 10.5120/ijca2021921383,
author = { Jingyuan Dai, Shahram Latifi },
title = { A Deep Learning Framework for Prediction of the Mechanism of Action },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2021 },
volume = { 183 },
number = { 12 },
month = { Jun },
year = { 2021 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2021921383 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:16:34.387916+05:30
%A Jingyuan Dai
%A Shahram Latifi
%T A Deep Learning Framework for Prediction of the Mechanism of Action
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 12
%P 1-7
%D 2021
%I Foundation of Computer Science (FCS), NY, 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.

  1. Masoudi-Nejad A, Mousavian Z, Bozorgmehr JH. “Drug-target and disease networks: polypharmacology in the post-genomic era.” In Silico Pharmacol. 2013; 1():17.
  2. Dickson M, Gagnon JP. “Key factors in the rising cost of new drug discovery and development”. Nat Rev Drug Discov. 2004; 3(5):417-29.
  3. Paul SM, Mytelka DS, Dunwiddie CT, et al. “How to improve R&D productivity: the pharmaceutical industry's grand challenge”. Nat Rev Drug Discov. 2010; 9(3):203-14.
  4. Maryam B, Elyas S, Kai W, et al. “Machine learning approaches and databases for prediction of drug–target interaction: a survey paper”. Briefings in Bioinformatics. 2021; 1(22); 247–269.
  5. Ding H, Takigawa I, Mamitsuka H, et al. “Similarity-based machine learning methods for predicting drug-target interactions: a brief review.” Brief Bioinform 2014;15:734–47.
  6. Cheng T, Hao M, Takeda T, et al. “Large-scale prediction of drug-target interaction: a data-centric review.” AAPS J. 2017;19:1264–75.
  7. Ezzat A, Wu M, Li XL, et al. “Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.” Brief Bioinform 2019;20:1337–1357.
  8. Chen X, Yan CC, Zhang X, et al. “Drug-target interaction prediction: databases, web servers and computational models.” Brief Bioinform 2016;17:696–712.
  9. Yamanishi Y, Araki M, Gutteridge A, et al. “Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.” Bioinformatics. 2008; 24(13):i232-40.
  10. Perlman L, Gottlieb A, Atias N, et al. “Combining drug and gene similarity measures for drug-target elucidation.” J Comput Biol 2011;18(2):133–45.
  11. Zhang W, Zou H, Luo L, et al. “Predicting potential side effects of drugs by recommender methods and ensemble learning.” Neurocomputing. 2016;173:979–987
  12. Zhang W, Chen Y, Liu F, et al. “Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data.” BMC Bioinformatics. 2017; 18(1):18.
  13. Wassermann AM, Geppert H, Bajorath J. “Ligand prediction for orphan targets using support vector machines and various target-ligand kernels is dominated by nearest neighbor effects”. J Chem Inf Model 2009;49(10):2155–67.
  14. Niu YQ. “Supervised prediction of drug–target interactions by ensemble learning”. J Chem Pharm Res 2014;6:1991–9.
  15. Shang Z, Jin L, Jiang Y, et al. “A method of drug target prediction based on SVM and its application”. Prog Modern Biomed 2012;20.
  16. Zhang P, Wang F, Hu J, et al. “Label Propagation Prediction of Drug-Drug Interactions Based on Clinical Side Effects”. Sci Rep. 2015 Jul 21; 5():12339.
  17. Li ZR, Lin HH, Han LY, et al. “PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence”. Nucleic Acids Res. 2006;34(Web Server issue):W32-W37. doi:10.1093/nar/gkl305
  18. Luo Y, Zhao X, Zhou J, et al. “A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information”.Nat Commun. 2017 ; 8(1):573.
  19. Wan F., Hong L., Xiao A., et al. “Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions”. Bioinformatics. 2018
  20. Wang Y, Zeng J. “Predicting drug-target interactions using restricted Boltzmann machines.” Bioinformatics. 2013 ; 29(13):i126-34.
  21. Gönen M. “Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization”. Bioinformatics. 2012; 28(18):2304-10.
  22. Cobanoglu MC, Liu C, Hu F et al. “Predicting drug-target interactions using probabilistic matrix factorization”. 2013; 53(12):3399-409
  23. Liu Y, Wu M, Miao C et al. “Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction”. PLoS Comput Biol. 2016; 12(2):e1004760.
  24. Hao M, Bryant SH, Wang Y. “Predicting drug-target interactions by dual-network integrated logistic matrix factorization”. Sci Rep. 2017; 7():40376.
  25. Zhou L, Li Z, Yang J, et al. “Revealing Drug-Target Interactions with Computational Models and Algorithms”. Molecules. 2019;24(9):1714.
  26. Xia Z, Wu LY, Zhou X et al. “Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces”. BMC Syst Biol. 2010; 4 Suppl 2():S6.
  27. Sergey S and Christian S, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”. arXiv:1502.03167
  28. Srivastava N, Hinton G, Krizhevsky A, et al. “Dropout: a simple way to prevent neural networks from overfitting.” The journal of machine learning research 15.1 (2014): 1929-1958.
  29. Tim S and Diederik P. K, “Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks”. 30th Conference on Neural Information Processing Systems (NIPS) 2016
  30. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
  31. Snoek J, Hugo L, and Ryan P. A. “Practical bayesian optimization of machine learning algorithms.” arXiv preprint arXiv:1206.2944 (2012).
  32. Mechanisms of Action (MoA) Prediction,Kaggle,
  33. Agarap, Abien F. "Deep learning using rectified linear units (relu)." arXiv preprint arXiv: 1803.08375 (2018).
Index Terms

Computer Science
Information Sciences


Convolutional Neural Nets Genetic Expression Drugs Mechanism of Actions Residual Networks