Abstract
Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Frankenfield J (2021) Artificial intelligence. Retrieved from: https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp#:~:text=Artificial%20intelligence%20(AI)%20refers%20to,as%20learning%20and%20problem%2Dsolving
McCarthy J (2004) What is Artificial Intelligence? Retrieved from: http://www-formal.stanford.edu/jmc/whatisai.pdf
Panesar A (2020) What is artificial intelligence? In: Machine learning and AI for healthcare. pp 1–18
Bishop CM (2013) Model-based machine learning. Philos Trans A Math Phys Eng Sci 371:20120222
Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AWR, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Kohli P, Jones DT, Silver D, Kavukcuoglu K, Hassabis D (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706–710
Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D (2020) Improved protein structure prediction using predicted interresidue orientations. Proc Natl Acad Sci U S A 117:1496–1503
Hessler G, Baringhaus KH (2018) Artificial intelligence in drug design. Molecules 23(10):2520
Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297
Breiman L (2001) Random forests. Mach Learn 45:5–32
Duda RO, Hart PE, Stork GE (2001) Pattern classification, 2nd edn. Wiley, New York, NY, pp 20–83
Zhang L, Tan J, Han D, Zhu H (2017) From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today 22:1680–1685
Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V (2015) Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 55:263–274
Unterthiner T, Mayr A, Klambauer G, Steijaert M, Ceulemans H, Wegner J, Hochreiter S (2014) Deep learning as an opportunity in virtual screening. Proceedings of the NIPS workshop on deep learning and representation learning, Montreal, QC, Canada. 8–13 December 2014. Accessed 15 Sept 2018, pp 1058–1066
Mayr A, Klambauer G, Unterthiner T, Hochreither S (2016) Deep Tox: toxicity prediction using deep learning. Front Environ Sci 2016:3
Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK (2021) Artificial intelligence in drug discovery and development. Drug Discov Today 26:80–93
Huang PS, Boyken SE, Baker D (2016) The coming of age of de novo protein design. Nature 537:320–327
Hartenfeller M, Schneider G (2011) Enabling future drug discovery by de novo design. WIREs Comput Mol Sci 1:742–759
Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98
Ertl P, Schuffenhauer AJ (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. ChemInform 1(1):8
Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4:120–131
Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018) Generative recurrent networks for de novo drug design. Mol Inf 37:1700111
Muller AT, Hiss JA, Schneider G (2018) Recurrent neural network model for constructive peptide design. J Chem Inf Model 58:472–479
Jabbari P, Rezaei R (2019) Artificial intelligence and immunotherapy. Expert Rev Clin Immunol 15:689–691
Hepler NL, Scheffler K, Weaver S et al (2014) IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform. PLoS Comput Biol 10(9):e1003842
Pavillon N, Hobro AJ, Akira S et al (2018) Noninvasive detection of macrophage activation with single-cell resolution through machine learning. Proc Nat Acad Sci 115:E2676–E2685
Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191
Moghram BA, Nabil E, Badr A (2018) Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design. Comput Methods Prog Biomed 153:161–170
Nagpal G, Chaudhary K, Agrawal P et al (2018) Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants. J Transl Med 16(1):181
Nagpal G, Gupta S, Chaudhary K et al (2015) VaccineDA: prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Sci Rep 5:12478
Dash R, Das R, Junaid M et al (2017) In silico-based vaccine design against Ebola virus glycoprotein. Adv Appl Bioinf Chem 10:11–28
Heinson AI, Gunawardana Y, Moesker B et al (2017) Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. Int J Mol Sci 18(2):312
Daubenberger CA (2007) TLR9 agonists as adjuvants for prophylactic and therapeutic vaccines. Curr Opin Mol Ther 9:45–52
Ahuja AS, Reddy VP, Marques O (2020) Artificial intelligence and COVID-19: a multidisciplinary approach. Integr Med Res 9(3):100434
Lee EK, Nakaya HI, Yuan F, Querec TD, Burel G, Pietz FH, Benecke BA, Pulendran B (2016) Machine learning for predicting vaccine immunogenicity. INFORMS J Appl Anal 46:368–390
Liu T, Shi K, Li W (2020) Deep learning methods improve linear B-cell epitope prediction. BioData Min 13:1
Chen B, Khodadoust MS, Olsson N et al (2019) Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol 37:1332–1343
McGowan E, Rosenthal R, Fiore-Gartland A, Macharia G, Balinda S, Kapaata A, Umviligihozo G, Muok E, Dalel J, Streatfield CL, Coutinho H, Dilernia D, Monaco DC, Morrison D, Yue L, Hunter E, Nielsen M, Gilmour J, Hare J (2021) Utilizing computational machine learning tools to understand immunogenic breadth in the context of a CD8 T-cell mediated HIV response. Front Immunol 12:609884
Dimitrov I, Zaharieva N, Doytchinova I (2020) Bacterial immunogenicity prediction by machine learning methods. Vaccine 8(4):709
Thomas S (2020) The structure of the membrane protein of SARS-CoV-2 resembles the sugar transporter semiSWEET. Pathog Immun 5(1):342–363
Thomas S (2021) Mapping the non-structural transmembrane proteins of SARS-CoV-2. J Comp Biol 28:909–921
Lu Wang L, Lo K, Chandrasekhar Y, Reas R, Yang J, Eide D, Funk K, Kinney R, Liu Z, Merrill W, Mooney P, Murdick D, Rishi D, Sheehan J, Shen Z, Stilson B, Wade AD, Wang K, Wilhelm C, Xie B, Raymond D, Weld DS, Etzioni O, Kohlmeier S (2020) CORD-19: the Covid-19 open research dataset. ArXiv [preprint]. 2020 Apr 22:arXiv:2004.10706v2
Fast E, Chen B (2020) Potential T-cell and B-cell epitopes of 2019-nCoV. bioRxiv [preprint]
Malone B, Simovski B, Moliné C, Cheng J, Gheorghe M, Fontenelle H, Vardaxis I, Tennøe S, Malmberg JA, Stratford R, Clancy T (2020) Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs. Sci Rep 10(1):22375
Kabra R, Singh S (1867) Evolutionary artificial intelligence based peptide discoveries for effective Covid-19 therapeutics. Biochim Biophys Acta Mol basis Dis 2021(1):165978
Dai W, Zhang B, Jiang XM, Su H, Li J, Zhao Y et al (2020) Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease. Science 368:1331–1335
Mohapatra S, Nath P, Chatterjee M, Das N, Kalita D, Roy P, Satapathi S (2020) Repurposing therapeutics for COVID-19: rapid prediction of commercially available drugs through machine learning and docking. PLoS One 15(11):e0241543
Kumari M, Subbarao N (2021) Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases. Comput Biol Med 132:104317
Zhang Y, Tang LV (2021) Overview of targets and potential drugs of SARS-CoV-2 according to the viral replication. J Proteome Res 20(1):49–59
Esmail S, Danter W (2021) Viral pandemic preparedness: a pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing. Stem Cells Transl Med 10(2):239–250
Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, Wang M, Qiu X, Li H, Yu H, Gong W, Bai Y, Li L, Zhu Y, Wang L, Tian J (2020) A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J 56(2):2000775
Poongodi M, Hamdi M, Malviya M, Sharma A, Dhiman G, Vimal S (2021) Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods. Pers Ubiquitous Comput 26:1–11
Liang W, Yao J, Chen A, Lv Q, Zanin M, Liu J, Wong S, Li Y, Lu J, Liang H, Chen G, Guo H, Guo J, Zhou R, Ou L et al (2020) Early triage of critically ill COVID-19 patients using deep learning. Nat Commun 11(1):3543
Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2021) Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 14:4–15
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Thomas, S., Abraham, A., Baldwin, J., Piplani, S., Petrovsky, N. (2022). Artificial Intelligence in Vaccine and Drug Design. In: Thomas, S. (eds) Vaccine Design. Methods in Molecular Biology, vol 2410. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1884-4_6
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1884-4_6
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1883-7
Online ISBN: 978-1-0716-1884-4
eBook Packages: Springer Protocols