Application of Artificial Intelligence in Biochemistry and Biomedical Sciences: A Review

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Published: 2022-09-03

Page: 302-312

Arowora, Adebisi Kayode

Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, P.M.B. 1020, Taraba State, Nigeria.

Imo Chinedu

Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, P.M.B. 1020, Taraba State, Nigeria.

Anih, David Chinonso

Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, P.M.B. 1020, Taraba State, Nigeria.

Moses, Abah Adondua *

Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, P.M.B. 1020, Taraba State, Nigeria.

Ugwuoke, Kenneth Chinekwu

Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, P.M.B. 1020, Taraba State, Nigeria.

*Author to whom correspondence should be addressed.


This review looked at how artificial intelligence is being used in biochemistry and other biological fields. It explains how computers and the applications that go along with them can be used to solve difficult healthcare problems and how computers can be used to interpret data from the diagnosis of various chronic diseases like Alzheimer's, diabetes, cardiovascular disease, and different types of cancer, such as breast and colon cancer. The review identified a number of automated systems and tools that assist to reduce mistakes and regulate the course of disease, including brain computer interfaces (BCIs), arterial spin labeling imaging (ASL-MRI), biomarkers, IT bra, and other algorithms. It was also shown that software implementation, expert systems, decision support systems, and computer aided diagnosis may help doctors reduce inter- and intraobserver variability. Artificial intelligence techniques, more especially artificial neural networks (ANN), fuzzy approach, may be used to manage many sorts of medical data in order to streamline the diagnostic process. The ANN approach is useful for building clinical assistance systems because it reveals hidden patterns and connections in medical data. Artificial intelligence (AI) is used in biochemistry for a variety of purposes, including the prediction of protein secondary structures, drug delivery, and enzyme creation. The value of AI in enhancing a variety of human endeavors cannot be overstated, hence efforts should be made to promote its use in biochemistry, the biomedical sciences, and other fields.

Keywords: Biomarkers, enzyme engineering, genes, artificial intelligence, computers

How to Cite

Kayode, A. A., Chinedu, I., Chinonso, A. D., Adondua, M. A., & Chinekwu, U. K. (2022). Application of Artificial Intelligence in Biochemistry and Biomedical Sciences: A Review. Asian Research Journal of Current Science, 4(1), 302–312. Retrieved from


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Weng J, McClelland J, Pentland A, Sporns O, Stockman IM. Autonomous mental development by robots and animals. Science. 2001;291(5504): 599-600.

Wooldridge M, Jennings NR. Intelligent agents: theory and practice. Knowl Eng Rev.1995;10:(2) 115-152.

Minsky M. Steps toward artificial intelligence. Proc IRE. 1961;49:(1)8-30.

Huang G, Huang GB, Song S, You K. Trends in extreme learning machines: A Review Neural Netw. 2015;61:32-48.

Avijeet B. Top 10 Artificial intelligence applications. Simplilearn. 2021;1-7.

Eban S. What are the three types of AI? A guide to narrow, general, and superartificial intelligence. Codebots. 2017;1-2.

Grosso C, Valentao P, Ferreres F. The use of flavonoids in central nervous system disorders. Curr Med Chem. 2013;20(37):4694-4719.

Barua A, Ghosh M, Kar N. Prevalence of depressive disorders in the elderly. Ann Saudi Med.2011;31(6): 620-4.


Drozdetskiy A, Cole C, Procter J, Barton GJ. A protein secondary structure prediction server. Nucleic acids research. 2015;43(W1):W389– W394.

Cummings JL, Isaacson RS, Schmitt. FA. A practical algorithm for managing's disease: What, when and why. Ann Clin Transl Neurol. 2015;2(3):307-323.

Liu TT, Brown GG. Measurement of cerebral perfusion with arterial spin labeling: Part 1. Methods. J. Int. Neuropsychol. Soc. 2007;13:(03):517-525.

Deursen JAV, Vuurman EFPM, Verhey FRJ. Increased EEG gamma band activity in Alzheimer’s disease and mild cognitive impairment. JNeural Transm. 2008;115(9): 1301-11.


Dauwels J, Vialatte F, Cichocki A. Diagnosis of Alzheimer's disease from EEG signals: where are we standing? Curr. Res. 2010;7(6):487-505.


Small GW, West J. Med. 1999;171:(5-6):293.

Yang J, Huang SC, Mega M. Investigation of partial volume correction methods for brain FDG PET studies. IEEE Trans Nucl Sci.1996;43(6):3322-7.

Coimbra A, Williams DS, Hostetler ED. The Role of MRI and PET/SPECT in Alzheimers Disease. Curr. Top. Med. Chem. 2006;6(6):629-647.

Sánchez CI, Niemeijer M, Dumitrescu AV. Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data. Invest. Ophthalmol Vis. Sci. 2011;52(7):4866-71.

Ferreira A, Morgado AM, Silva JS. Automatic corneal nerves recognition for earlier diagnosis and follow-up of diabetic neuropathy. In: International Conference Image Analysis and Recognition Springer Berlin Heidelberg. 2010;60-69.

Marateb HR, Mansourian M, Faghihimani E. A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. 2014;45:34-42.

Cho BH, Yu H, Kim K. Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods. ArtifIntell Med.2008;42(1):37-53.

Gotzsche PC, Jorgensen K.J. Screening for breast cancer with mammography. Cochrane Database Syst. Rev. 2013; 6:CD001877.

Fujita H, Uchiyama Y, Nakagawa T. Computer-aided diagnosis: The emerging of three CAD systems induced by Japanese health care needs. Comput Methods Programs Biomed. 2008; 92(3):238-48.

Dandolu V, Hernandez E. Mammographic breast density. New Engl J Med. 2007; 356(18):1885-87.

Rakow-Penner R, Abdelgelil N, Eghtedari M. Curemetrix La Jolla. 2016; 92037.

Saritas I. Prediction of breast cancer using artificial neural networks. J Med Syst. 2012;36(5):2901-2907.

Seker H, Odetayo MO, Petrovic D. Soft feature evaluation indices for the identification of significant image cytometric factors in assessment of nodal involvement in breast cancer patients. Anticancer Res.2002;22:433-8.

Sizilio GR, Leite CR, Guerreiro AM. Fuzzy method for pre diagnosis of breast cancer from the fine needle aspirate analysis. Biomed Eng Online. 2012;11(1):83.

Kong L, Zhang Y, Ye ZQ, Liu XQ, Zhao SQ, Wei L, Gao G. Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res. 2007;35: W345.

Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, Liu Y, Chen R, Zhao Y. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res. 2013;41.

Li A, Zhang J, Zhou Z. A tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinform.2014;15:31.

Mariner PD, Walters RD, Espinoza CA, Drullinger LF, Wagner SD, Kugel J.F, Goodrich JA. Human Alu RNA Is a Modular Transacting Repressor of mRNA Transcription during Heat Shock. Mol.Cell. 2008;29: 499-509.

Xulvi-Brunet R, Li H. Co-expression networks: Graph properties and topological comparisons. Bioinformatics. 2010;26: 205–214.

Fan XN, Zhang SW. Identification of human long non-coding RNAs by fusing multiple features and using deep learning. Mol. Biosyst. 2015;11: 892– 897.

Pian C, Zhang G, Chen Z, Chen Y, Zhang J, Yang T, Zhang L. Classification of long non-coding RNAs and protein-coding transcripts by the ensemble algorithm with a new hybrid feature. PLoS ONE.2016;11.

Yu N, Yu Z, Pan Y. A deep learning method for lincRNA detection using auto-encoder algorithm. BMC Bioinform. 2017; 18:511.

Campbell AM, Heyer LJ. Discovering genomics, proteomics and bioinformatics. CHSL Press, Benjamin Cummings; San Francisco; 2003.

Cuff JA, Barton GJ. Evaluation and Improvement of Multiple Sequence Methods for Protein Secondary Structure Prediction, PROTEINS: Structure, Function, and Genetics. 2000;199934508–519.

Avdagic Z, Purisevic E. Proccedings: Cybernetics and systems. Vienna: Austrian Society for Cybernetic Study; Feed-Forward Neural Network for Protein Structure Prediction. 2006;1:198.

Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019;24:773–780.

Sellwood MA. Artificial intelligence in drug discovery. Fut. Sci. 2018;10:2025–2028

Ciallella HL, Zhu H. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity. Chem. Res. Toxicol. 2019;32:536–547.

Zhu H. Big data and artificial intelligence modeling for drug discovery. Annu. Rev. Pharmacol. Toxicol. 2020;60:573–589.

Chan, HS. Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 2019;40(8):592–604.

Alzheimer's disease International. Policy Brief for G8 Heads of Government: The Global Impact of Dementia. 2013; 2013-50.

Pereira JC. Boosting docking-based virtual screening with deep learning. J. Chem. Inf. Model. 2016;56:2495–2506.

Firth NC. An integrated workflow for multiobjective optimization:implementation, synthesis, and biological evaluation. J. Chem. Inf. Model. 2015;55:1169–1180.

Zhang C, Freddolino PL, Zhang Y. COFACTOR: Improved protein function prediction by combining structure, sequence and protein–protein interaction information. Nucleic Acids Res. 2017;45: 10:1093.

Wang Y. A comparative study on family-specific protein–ligand complex affinity prediction based on random forest approach. J. Comput. -Aided Mol. Des. 2015;29:349.

King RD. Comparison of artificial intelligence methods for modeling pharmaceutical QSARS. Appl. Artif. Intell. 1995;9:213–233.

UniProt Consortium UniProt. A Worldwide Hub of Protein Knowledge. Nucleic Acids Res. 2018;47:506-515.

Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Zidek A, Nelson A, Bridgland A, PenedonesH, Petersen S, Simonyan K, Jones DT, Silver D, Kavukcuoglu K, Hassabis D, Senior, AW. De Novo Structure Prediction with Deep Learning Based Scoring.

Kinch LN, Shi S, Cheng H, Cong Q, Pei J, Mariani V, Schwede T, Grishin, NV. CASP9 Target Classification. Proteins: Struct., Funct., Genet. 2011;79:21– 36.

Shehu A, Barbará D, Molloy K. A survey of computational methods for protein function prediction, Springer. 2016;225–298.

Kumar N, Skolnick J. Application of a high-precision enzyme function predictor to 396 proteomes. Bioinformatics. 2012;28: 2687–2688.

Li Y, Wang S, Umarov R, Xie B, Fan M, Li L, Gao X. DEEPre: Sequence-based enzyme ec number prediction by deep learning. Bioinformatics. 2018;34:769.

Yang Y, Urolagin S, Niroula A, Ding X, Shen B, Vihinen M. PON-: Protein variant stability predictor. importance of training data quality. Int. J. Mol. Sci. 2018;19: 1009.