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A Review of Deep Learning-Based Approaches for Detection and Diagnosis of Diverse Classes of Drugs

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Abstract

Artificial intelligence-based drug discovery has gained attention lately since it drastically cuts the time and money needed to produce new treatments. In recent years, a vast quantity of data in various formats has been made accessible in the medical field to analyse different health complications. Drug discovery aims to uncover possible novel medications using a multidisciplinary approach that includes biology, chemistry, and pharmacology. Traditional sentiment analysis methods count or repeat words in a text assigned sentiment ratings by an expert. Several outdated, ineffective old methodologies are utilized to forecast drug design and discovery. However, with the development of DL (deep learning), the traditional drug discovery method has been further simplified. In this work, we applied deep learning models, such as LSTM (Long short-term memory), GRU (Gated recurrent units), Bidirectional LSTM (BiLSTM), Bidirectional GRU(BiGRU), SimpleRNN, embedding + LSTM, embedding + GRU, embedding + GRU + dropout, embedding + conv1d + LSTM, and Embedding + Conv1d + GRU on a dataset of drug reviews. Furthermore, we used Adam and RMSprop, two optimizers, for each model, for increased optimization. This research focuses on categorizing medication reviews into positive and negative categories. The effectiveness of the different deep learning models was assessed using a wide range of performance measures. Experiments demonstrated that the GRU (Gated Recurrent Unit) generated exceptional validation dataset results. In addition, this study emphasizes the relevance of deep learning methods over traditional learning approaches in categorization.

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References

  1. Reddy AS, Zhang S (2013) Polypharmacology: drug discovery for the future. Expert Rev Clin Pharmacol 6(1):41–47

    Article  Google Scholar 

  2. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, …, Zhao S (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discovery 18(6):463–477

    Article  Google Scholar 

  3. Kimber TB, Chen Y, Volkamer A (2021) Deep learning in virtual screening: recent applications and developments. Int J Mol Sci 22(9):4435

    Article  Google Scholar 

  4. Feng Q, Dueva E, Cherkasov A, Ester M (2018) Padme: A deep learning-based framework for drug-target interaction prediction. arXiv preprint arXiv:1807.09741

  5. Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A.,Aladinskaya, A. V., … Aspuru-Guzik, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature biotechnology, 37(9):1038–1040

    Article  Google Scholar 

  6. Shang C, Liu Q, Chen KS, Sun J, Lu J, Yi J, Bi J (2018) Edge attention-based multi-relational graph convolutional networks. arXiv preprint arXiv: 1802.04944

  7. Chandrasekaran B, Abed SN, Al-Attraqchi O, Kuche K, Tekade RK (2018) Computer-aided prediction of pharmacokinetic (ADMET) properties. In: Gowtham P (eds) Dosage form design parameters. Academic Press, Cambridge

    Google Scholar 

  8. Bender A, Cortes-Ciriano I (2021) Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. Drug Discovery Today 26(4):1040–1052

    Article  Google Scholar 

  9. Gardner S, Das S, Taylor K (2020) AI enabled precision medicine: patient stratification, drug repurposing and combination therapies. 

    Google Scholar 

  10. Basiri ME, Abdar M, Cifci MA, Nemati S, Acharya UR (2020) A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowl Based Syst 198:105949

    Article  Google Scholar 

  11. Long H, Wang M, Fu H (2017) Deep convolutional neural networks for predicting hydroxyproline in proteins. Curr Bioinform 12(3):233–238

    Article  Google Scholar 

  12. Korkmaz S (2020) Deep learning-based imbalanced data classification for drug discovery. J Chem Inf Model 60(9):4180–4190

    Article  Google Scholar 

  13. Urban, G., Bache, K., Phan, D. T., Sobrino, A., Shmakov, A. K., Hachey, S. J., … Baldi,P. (2018). Deep learning for drug discovery and cancer research: Automated analysis of vascularization images. IEEE/ACM transactions on computational biology and bioinformatics, 16(3): 1029–1035

    Article  Google Scholar 

  14. Hu S, Chen P, Gu P, Wang B (2020) A deep learning-based chemical system for QSAR prediction. IEEE J biomedical health Inf 24(10):3020–3028

    Article  Google Scholar 

  15. Zhao K, So HC (2018) Drug repositioning for schizophrenia and depression/anxiety disorders: a machine learning approach leveraging expression data. IEEE J biomedical health Inf 23(3):1304–1315

    Article  Google Scholar 

  16. Liu T, Khuri N (2021), March Classification of drug prescribing information using long short-term memory networks. In Proceedings of the 36th Annual ACM Symposium on Applied Computing (pp. 1086–1089)

  17. Zhang M, Geng G (2019) Adverse drug event detection using a weakly supervised convolutional neural network and recurrent neural network model. Information 10(9):276

    Article  Google Scholar 

  18. Lee CY, Chen YPP (2021) Descriptive prediction of drug side-effects using a hybrid deep learning model. Int J Intell Syst 36(6):2491–2510

    Article  MathSciNet  Google Scholar 

  19. Gräßer F, Kallumadi S, Malberg H, Zaunseder S (2018), April Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In Proceedings of the 2018 International Conference on Digital Health (pp. 121–125)

  20. Yadav S, Ekbal A, Saha S, Bhattacharyya P (2018), May Medical sentiment analysis using social media: towards building a patient assisted system. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

  21. Kawano, S., Ito, K., Yahata, K., Kira, K., Abe, T., Akagi, T., … Kishi, Y. (2019). A landmark in drug discovery based on complex natural product synthesis. Scientific Reports, 9(1): 1–9

    Article  Google Scholar 

  22. Cui F, Zhang Z, Zou Q (2021) Sequence representation approaches for sequence-based protein prediction tasks that use deep learning. Brief Funct Genomics 20(1):61–73

    Article  Google Scholar 

  23. Liu Z, Roberts RA, Lal-Nag M, Chen X, Huang R, Tong W (2021) AI-based language models powering drug discovery and development. Drug Discovery Today 26(11):2593–2607

    Article  Google Scholar 

  24. Gajbhiye A, Jaf S, Moubayed NA, McGough AS, Bradley S (2018), October An exploration of dropout with rnns for natural language inference. In International conference on artificial neural networks (pp. 157–167). Springer, Cham

  25. Goldberg Y (2016) A primer on neural network models for natural language processing. J Artif Intell Res 57:345–420

    Article  MathSciNet  MATH  Google Scholar 

  26. Tao J, Zhang X, Lin X (2022) A Targeted Drug Design Method Based on GRU and TopP Sampling Strategies. In International Conference on Intelligent Computing (pp. 423–437). Springer, Cham

  27. Carvalho AS (2019) Recurrent Models for Drug Generation (Doctoral dissertation, Universidade de Coimbra)

  28. Liu X, Wang Y, Wang X, Xu H, Li C, Xin X (2021) Bi-directional gated recurrent unit neural network based nonlinear equalizer for coherent optical communication system. Opt Express 29(4):5923–5933

    Article  Google Scholar 

  29. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555

  30. Baskin II, Winkler D, Tetko IV (2016) A renaissance of neural networks in drug discovery. Expert Opin Drug Discov 11(8):785–795

    Article  Google Scholar 

  31. Kaur I, Sandhu AK, Kumar Y (2022) Artificial Intelligence Techniques for Predictive modeling of Vector-Borne Diseases and its pathogens: a systematic review. Archives of Computational Methods in Engineering, 29: 1–31

    MathSciNet  Google Scholar 

  32. Kumar Y, Gupta S, Singh W (2022) A novel deep transfer learning models for recognition of birds sounds in different environment. Soft Comput 26(3):1003–1023

    Article  Google Scholar 

  33. Wang X, Liu J, Zhang C, Wang S (2022) SSGraphCPI: a Novel Model for Predicting compound-protein interactions based on deep learning. Int J Mol Sci 23(7):3780

    Article  Google Scholar 

  34. Liu Y, De Vijlder T, Bittremieux W, Laukens K, Heyndrickx W (2021) Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation. Rapid Commun Mass Spectrom. https://doi.org/10.1002/rcm.9120

    Article  Google Scholar 

  35. Tong X, Liu X, Tan X, Li X, Jiang J, Xiong Z, Zheng M (2021) Generative models for De Novo drug design. J Med Chem 64(19):4011–14027

    Article  Google Scholar 

  36. Stravs MA, Dührkop K, Böcker S, Zamboni N (2022) MSNovelist: de novo structure generation from mass spectra. Nat Methods 19(7):1–6

    Article  Google Scholar 

  37. Basile AO, Yahi A, Tatonetti NP (2019) Artificial intelligence for drug toxicity and safety. Trends Pharmacol Sci 40(9):624–635

    Article  Google Scholar 

  38. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discovery Today 23(6):1241–1250

    Article  Google Scholar 

  39. Tembhurne JV, Diwan T (2021) Sentiment analysis in textual, visual and multimodal inputs using recurrent neural networks. Multimedia Tools Appl 80(5):6871–6910

    Article  Google Scholar 

  40. Baytas IM, Xiao C, Zhang X, Wang F, Jain AK, Zhou J (2017), August Patient subtyping via time-aware LSTM networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 65–74)

  41. Kumar Y, Gupta S (2023) Deep transfer learning approaches to predict Glaucoma, cataract, Choroidal Neovascularization, Diabetic Macular Edema, DRUSEN and healthy eyes: an experimental review. Arch Computat Methods Eng 30:521–541. https://doi.org/10.1007/s11831-022-09807-7

    Article  Google Scholar 

  42. Li X, Xu C, Wang K, Liu Z, Li G (2022) Prediction of outlet pressure for the sulfur dioxide blower based on conv1d-bigru model and genetic algorithm. Comput Intell Neurosci. https://doi.org/10.1155/2022/6297746

    Article  Google Scholar 

  43. Bhardwaj P, Bhandari G, Kumar Y et al (2022) An investigational approach for the prediction of gastric cancer using artificial intelligence techniques: a systematic review. Arch Computat Methods Eng 29:4379–4400. https://doi.org/10.1007/s11831-022-09737-4

    Article  Google Scholar 

  44. Habib M, Faris M, Qaddoura R, Alomari A, Faris H (2021) A predictive text system for medical recommendations in telemedicine: a deep learning approach in the arabic context. IEEE Access 9:85690–85708

    Article  Google Scholar 

  45. Kaur I, Sandhu AK, Kumar Y (2022) Artificial Intelligence Techniques for Predictive modeling of Vector-Borne Diseases and its pathogens: a systematic review. Arch Computat Methods Eng 29:3741–3771. https://doi.org/10.1007/s11831-022-09724-9

    Article  MathSciNet  Google Scholar 

  46. Koul A, Bawa RK, Kumar Y (2022) Artificial Intelligence in Medical Image Processing for Airway Diseases. In: Mishra S, González-Briones A, Bhoi AK, Mallick PK, Corchado JM (eds) Connected e-Health. Studies in Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-97929-4_10

    Chapter  Google Scholar 

  47. Kaur S, Kumar Y, Koul A et al (2023) A systematic review on Metaheuristic optimization techniques for feature selections in Disease diagnosis: Open Issues and Challenges. Arch Computat Methods Eng 30:1863–1895. https://doi.org/10.1007/s11831-022-09853-1

    Article  Google Scholar 

  48. Kumar Y, Koul A, Mahajan S (2022) A deep learning approaches and fastai text classification to predict 25 medical diseases from medical speech utterances, transcription and intent. Soft Comput 26:8253–8272. https://doi.org/10.1007/s00500-022-07261-y

    Article  Google Scholar 

  49. Bansal K, Bathla RK, Kumar Y (2022) Deep transfer learning techniques with hybrid optimization in early prediction and diagnosis of different types of oral cancer. Soft Comput 26:11153–11184. https://doi.org/10.1007/s00500-022-07246-x

    Article  Google Scholar 

  50. Kanna GP, Kumar SJKJ, Parthasarathi P et al (2023) A review on prediction and prognosis of the prostate Cancer and gleason grading of Prostatic Carcinoma using deep transfer learning based approaches. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-023-09896-y

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of CSE, Manipal University Jaipur, Jaipur, Rajasthan, India

    Ashish Kumar

  2. Department of CSE, Gurukula Kangri (Deemed to be University), Haridwar, Uttarakhand, India

    Nishant Kumar

  3. Mizuho Bank, Singapore, Singapore

    Jeril Kuriakose

  4. Department of CSE, School of Technology, Pandit Deendayal Energy University, Gujarat, Gandhinagar, India

    Yogesh Kumar

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  1. Ashish Kumar
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Kumar, A., Kumar, N., Kuriakose, J. et al. A Review of Deep Learning-Based Approaches for Detection and Diagnosis of Diverse Classes of Drugs. Arch Computat Methods Eng 30, 3867–3889 (2023). https://doi.org/10.1007/s11831-023-09936-7

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