Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset

Main Article Content

Nisreen Sulayman


The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA molecule. A sequence DL model based on a bidirectional gated recurrent unit (GRU) is implemented. The model is applied to the Stanford COVID-19 mRNA vaccine dataset to predict the mRNA sequences deterioration by predicting five reactivity values for every base in the sequence, namely reactivity values, deterioration rates at high pH, at high temperature, at high pH with Magnesium, and at high temperature with Magnesium. The Stanford COVID-19 mRNA vaccine dataset is split into the training set, validation set, and test set. The bidirectional GRU model minimizes the mean column wise root mean squared error (MCRMSE) of deterioration rates at each base of the mRNA sequence molecule with a value of 0.32086 for the test set which outperformed the winning models with a margin of (0.02112). This study would help other researchers better understand how to forecast mRNA sequence molecule properties to develop a stable COVID-19 vaccine.


Download data is not yet available.

Article Details

How to Cite
Sulayman N. Deep Learning-based Predictive Model of mRNA Vaccine Deterioration: An Analysis of the Stanford COVID-19 mRNA Vaccine Dataset. Baghdad Sci.J [Internet]. 2023 Aug. 30 [cited 2023 Oct. 4];20(4(SI):1451-8. Available from:
Special Issue - Current advances in anti-infective strategies


Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Bio Medica: Atenei Parmensis. 2020; 91(1): 157.

Sulayman N. Comparative study for automated coronavirus detection in CT images with transfer learning. Damascus Uni J Eng Sci, 2022; 38(5): 245-255.

Mohammed SK, Taha MM, Taha EM, Mohammad MN. Cluster analysis of biochemical markers as predictor of COVID-19 severity. Baghdad Sci J. 2022; 19(6 (Suppl.)): 1423-1429

Zaki SM, Jaber MM, Kashmoola MA. Diagnosing COVID-19 Infection in Chest X-Ray Images Using Neural Network. Baghdad Sci J. 2022; 19(6): 1356-1361.

Mohammed TS, Sultan AI, Alheeti KM, Aljebory KM, Sultan HI, Al-Ani MS. COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT. Baghdad Sci J. 2022; 0372-.

WHO Coronavirus (COVID-19) Dashboard, (accessed on 11 August 2022)

Corbett KS, Edwards DK, Leist SR, Abiona OM, Boyoglu-Barnum S, Gillespie RA, Himansu S, Schäfer A, Ziwawo CT, DiPiazza AT, Dinnon KH. SARS-CoV-2 mRNA vaccine design enabled by prototype pathogen preparedness. Nature. 2020; 586(7830): 567-71.

Kremsner PG, Mann P, Kroidl A, Leroux-Roels I, Schindler C, Gabor JJ, et al. Safety and immunogenicity of an mRNA-lipid nanoparticle vaccine candidate against SARS-CoV-2: A phase 1 randomized clinical trial. Wien Klin Wochenschr. 2021; 133(17-18): 931-41.

Ribonucleic Acid (RNA). (Accessed on 11 August 2022)

Lyngsø RB, Pedersen CN. RNA pseudoknot prediction in energy-based models. J Comput Biol. 2000; 7(3-4): 409-27.

Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines—a new era in vaccinology. Nat Rev Drug Discov. 2018; 17(4): 261-79.

Bouarara HA. N-Gram-Codon and Recurrent Neural Network (RNN) to Update Pfizer-BioNTech mRNA Vaccine. Int J Comput Intell Syst. 2022; 14(1): 1-24.

Ing SH, Abdullah AA, Harun NH, Kanaya S. COVID-19 mRNA Vaccine Degradation Prediction Using LR and LGBM Algorithms. J Phys Conf Ser. 2021; 1997(1): 012005. IOP Publishing.

Muneer A, Fati SM, Akbar NA, Agustriawan D, Wahyudi ST. iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning. J King Saud Univ- Comput. Inf Sci. 2022; 34(9): 7419-32.

Krishna UV, Premjith B, Soman KP. A Comparative Study of Pre-trained Gene Embeddings for COVID-19 mRNA Vaccine Degradation Prediction. Proc 7th Int Conf Math Comput : pp. 301-308. Singapore: Springer Singapore.

Imran SA, Islam MT, Shahnaz C, Islam MT, Imam OT, Haque M. COVID-19 mRNA Vaccine Degradation Prediction using Regularized LSTM Model. Int Women Eng Conf. 2020; (pp. 328-331). IEEE.

Openvaccine: Covid-19 mrna vaccine degradation prediction, Sep. 2020; competitions/stanford-covid-vaccine/data

Danaee P, Rouches M, Wiley M, Deng D, Huang L, Hendrix D. bpRNA: large-scale automated annotation and analysis of RNA secondary structure. Nucleic Acids Res. 2018; 46(11): 5381-94.

Qaid TS, Mazaar H, Alqahtani MS, Raweh AA, Alakwaa W. Deep sequence modelling for predicting COVID-19 mRNA vaccine degradation. Peer J Comput Sci. 2021; 7: e597. https://doi.org10.7717/peerj-cs.597

Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proc Conf Empir Methods Nat Lang Proc. 2014; 1406.1078.

Cho K, Van Merriënboer B, Bahdanau D, Bengio Y. On the properties of neural machine translation: Encoder-decoder approaches. Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. 2014; 1409.1259.

Schoenmaker L, Witzigmann D, Kulkarni JA, Verbeke R, Kersten G, Jiskoot W, et al. mRNA-lipid nanoparticle COVID-19 vaccines: Structure and stability. Int J Pharm X. 2021; 601:120586.