Acta Scientific Computer Sciences

Research Article Volume 4 Issue 3

A Deep Learning Multi-class Model for Drug-target Binding Affinity Prediction

Nassima Aleb*

Computer Science Department, Jubail University College, Jubail Industrial City, Kingdom of Saudi Arabia

*Corresponding Author: Nassima Aleb, Computer Science Department, Jubail University College, Jubail Industrial City, Kingdom of Saudi Arabia.

Received: November 25, 2021; Published: February 24, 2022


Drug design and discovery is a very challenging and costly process. It involves a crucial phase of drug-target interaction (DTIs) identification. Nevertheless, most existing methods use either binary classification to predict the presence of an interaction in a Drug-Target pair, or regression methods to predict the exact float-value representing the Binding Affinity. These latter methods are more valuable but suffer from unsatisfactory results despite their very sophisticated models and multiple inputs. In this paper, we present a new approach for predicting the strength of drug-target binding, we tackle the question as a Multi-class classification problem. This approach is very rational since the key points, in drug-target interaction, are to have a precise indication about the binding strength and to establish a ranking between drug-target pairs’ binding strengths. Our model input being sequences presenting hidden patterns, we use convolutional LSTM networks, since they inherit the ability in discovering patterns from Convolutional networks, and learning from sequential data from recurrent networks. Besides the usual performance metrics, we investigate new interesting performance metrics that have never been explored before. The results show that our approach is very convincing.

Keywords: Deep Learning Models for Drug Discovery; Drug Repurposing; Drug-target Binding Affinity; Discretization; Multi-Class Classification Models


  1. Thafar M., et al. “Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities”. Chemistry Frontiers 7 (2019): 782.
  2. Hu P Chan and KCC You Z. “Large-scale prediction of drug– target interactions from deep representations”. International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada. IEEE (2016): 1236-1243.
  3. Hamanaka M., et al. “Cgbvs-dnn: prediction of compound– protein interactions based on deep learning”. Molecular Informatics (2016): 36.
  4. Truchon JF and Bayly CI. “Evaluating virtual screening methods: good and bad metrics for the "early recognition" problem”. Journal of Chemical Information and Modeling2 (2007): 488-508.
  5. Carpenter KA., et al. “Deep learning and virtual drug screening”. Future Medicinal Chemistry21 (2018): 2557-2567.
  6. Feng Q. “PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction”. (Master thesis), Simon Fraser University, Burnaby, BC, Canada (2019).
  7. Ballester PJ and Mitchell JB. “A machine learning approach t predicting protein–ligand binding affinity with applications to molecular docking”. Bioinformatics 26 (2010): 1169-1175.
  8. F Li,H., et al. “Low-quality structural and interaction data improves binding affinity prediction via random forest”. Molecules 20 (2015): 10947-10962.
  9. Shar PA., et al. “Pred-binding: large-scale protein–ligand binding affinity prediction”. Journal of Enzyme Inhibition and Medicinal Chemistry 31 (2016): 1443-1450.
  10. Gabel J., et al. “Beware of machine learning-based scoring functions on the danger of developing black boxes”. Journal of Chemical Information and Modeling 54 (2014): 2807-2815.
  11. He T., et al. “Simboost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines”. Journal of Cheminformatics 9 (2017): 24.
  12. Pahikkala T., et al. “Toward more realistic drug-target interaction predictions”. Briefings in Bioinformatics (2015).
  13. Schmidhuber J. “Deep learning in neural networks: An overview”. Neural Networks 61 (2015): 85-117.
  14. Ciregan D., et al. “Multi-column deep neural networks for image classification”. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island. IEEE (2012): 3642-3649.
  15. Donahue J., et al. “Decaf: a deep convolutional activation feature for generic visual recognition”. In: ICML, Beijing, China (2014): 647-655.
  16. Simonyan K and Zisserman A. “Very deep convolutional networks for large-scale image recognition”. In: 3rd International Conference on Learning Representations (ICLR), Hilton San Diego Resort and Spa (2015): 7-9.
  17. Dahl GE., et al. “Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition”. IEEE Transactions on Audio, Speech, and Language Processing 20 (2012): 30-42.
  18. Graves A., et al. “Speech recognition with deep recurrent neural networks”. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada. IEEE (2013): 6645-6649.
  19. Hinton G., et al. “Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups”. IEEE Signal Processing Magazine 29 (2012): 82-97.
  20. Leung MK., et al. “Deep learning of the tissue-regulated splicing code”. Bioinformatics 30 (2014): i121-i129.
  21. Xiong HY., et al. “The human splicing code reveals new insights into the genetic determinants of disease”. Science 347 (2015): 1254806.
  22. Ma J., et al. “Deep neural nets as a method for quantitative structure–activity relationships”. Journal of Chemical Information and Modeling 55 (2015): 263-274.
  23. Jing Y., et al. “Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era”. The AAPS Journal 20 (2018): 58.
  24. Ekins S., et al. “Exploiting machine learning for end-to-end drug discovery and development”. Nature Materials 18 (2019): 435-441.
  25. LeCun Y Bengio and G Hinton. “Deep learning”. Nature7553 (2015): 436-444.
  26. Weininger D. “SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules”. Journal of Chemical Information and Modeling 28 (1988): 31-36.
  27. Krig S. “Feature learning and deep learning architecture survey”. In Computer Vision Metrics (2016): 375-514.
  28. Gomez-Bombarelli R., et al. “Automatic chemical design using a data-driven continuous representation of molecules”. ACS Central Science 4 (2018): 268-276.
  29. Jastrzkeski S., et al. “Learning to smile (s)”. Arxiv Preprint Arxiv (2016): 1602.06289.
  30. Gomes J., et al. “Atomic convolutional networks for predicting protein–ligand binding affinity”. Arxiv Preprint Arxiv (2017): 1703.10603.
  31. Ragoza M., et al. “Protein–ligand scoring with convolutional neural networks”. Journal of Chemical Information and Modeling 57 (2017): 942957.
  32. Wallach I., et al. “Atomnet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery”. Arxiv Preprint Arxiv (2015): 1510.02855.
  33. Öztürk H., et al. “DeepDTA: deep drug-target binding affinity prediction”. Bioinformatics 34 (2018): i821-i829.
  34. Öztürk H., et al. “WideDTA: prediction of drugtarget binding affinity”. Arxiv (2019): 1902.04166.
  35. Karimi M., et al. “DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks”. Bioinformatics 35 (2019): 3329-3338.
  36. Rogers D and Hahn M. “Extended-connectivity fingerprints”. Journal of Chemical Information and Modeling 50 (2010): 742-754.
  37. Liu K., et al. “Chemi-Net: a molecular graph convolutional network for accurate drug property prediction”. International Journal of Molecular Sciences 20 (2019): 3389.
  38. Gromiha M. “Protein Bioinformatics: From Sequence to Function”. New Delhi: Academic Press (2011).
  39. Yamanishi Y., et al. “Prediction of drug–target interaction networks from the integration of chemical and genomic spaces”. Bioinformatics 24 (2008): i232-i240.
  40. Davis MI., et al. “Comprehensive analysis of kinase inhibitor selectivity”. Nature Biotechnology 29 (2011): 1046-1051.
  41. Metz JT., et al. “Navigating the kinome”. Nature Chemical Biology 7 (2011): 200-202.
  42. Tang J., et al. “Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis”. Journal of Chemical Information and Modeling 54 (2014): 735-743.
  43. Weiss S. “Rule-base Regression”. In Proceedings of the 13th International Joint Conference on Artificial Intelligence (1993): 1072-1078.
  44. Indurkhya N. “Rule-based Machine Learning Methods for Functional Prediction”. In Journal of Artificial Intelligence Research (JAIR) 3 (1995): 383-403.
  45. Chollet F., et al. “Keras (2015).
  46. Kang L., et al. “Convolutional neural networks for no-reference image quality assessment”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA (2014): 1733-1740.
  47. Goodfellow I., et al. “Deep Learning”. MIT Press (2016).
  48. Gonen M and Heller G. “Concordance probability and discriminatory power in proportional hazards regression”. Biometrika 92 (2005): 965-970.
  49. Weston J and Chris W. “Support Vector Machines for Multi-Class Pattern Recognition”. European Symposium on Artificial Neural Networks (1999).
  50. D Abadi M., et al. “Tensorflow: a system for large- learning”. In: OSDI Scale Machine 16 (2016): 265-283.


Citation: Nassima Aleb. “A Deep Learning Multi-class Model for Drug-target Binding Affinity Prediction". Acta Scientific Computer Sciences 4.3 (2022): 14-24.


Copyright: © 2022 Nassima Aleb. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
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