Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

Origin and evolution of pathogenic coronaviruses

Cui, J., Li, F., & Shi, Z. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0118-9 · Cited by 5,539 · see citing works

A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

Gordon, D. E., Jang, G. M., Bouhaddou, M., Xu, J., Obernier, K., White, K. M., O’Meara, M. J., Rezelj, V. V., Guo, J. Z., Swaney, D. L., et al. (2020) · Nature · doi:10.1038/s41586-020-2286-9 · Cited by 4,405 · see citing works

Coronavirus Infections—More Than Just the Common Cold

Paules, C. I., Marston, H. D., & Fauci, A. S. (2020) · JAMA · doi:10.1001/jama.2020.0757 · Cited by 2,135 · see citing works

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas

Ren, X., Wen, W., Fan, X., Hou, W., Su, B., Cai, P., Li, J., Liu, Y., Tang, F., Zhang, F., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.053 · Cited by 865 · see citing works

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.025 · Cited by 815 · see citing works

Emerging Pandemic Diseases: How We Got to COVID-19

Morens, D. M., & Fauci, A. S. (2020) · Cell · doi:10.1016/j.cell.2020.08.021 · Cited by 720 · see citing works

Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity

Thomson, E. C., Rosen, L. E., Shepherd, J. G., Spreafico, R., da Silva Filipe, A., Wojcechowskyj, J. A., Davis, C., Piccoli, L., Pascall, D. J., Dillen, J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.037 · Cited by 606 · see citing works

deepDR: a network-based deep learning approach toin silicodrug repositioning

Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., & Cheng, F. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz418 · Cited by 532 · see citing works

Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

Ton, A., Gentile, F., Hsing, M., Ban, F., & Cherkasov, A. R. (2020) · Molecular Informatics · doi:10.1002/minf.202000028 · Cited by 518 · see citing works

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

Ong, E., Wong, M. U., Huffman, A., & He, Y. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01581 · Cited by 499 · see citing works

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Wei, L., Zhou, C., Chen, H., Song, J., & Su, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty451 · Cited by 441 · see citing works

COVID-19 and multiorgan failure: A narrative review on potential mechanisms

Mokhtari, T., Hassani, F., Ghaffari, N., Ebrahimi, B., Yarahmadi, A., & Hassanzadeh, G. (2020) · Journal of Molecular Histology · doi:10.1007/s10735-020-09915-3 · Cited by 440 · see citing works

Target identification among known drugs by deep learning from heterogeneous networks

Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357 · see citing works

Author Correction: A new coronavirus associated with human respiratory disease in China

Wu, F., Su, Z., Yu, B., Chen, Y., Wang, W., Song, Z., Hu, Y., Tao, Z., Tian, J.-H., Pei, Y.-Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2202-3 · Cited by 318 · see citing works

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening

Basith, S., Manavalan, B., Shin, T. H., & Lee, G. (2020) · Medicinal Research Reviews · doi:10.1002/med.21658 · Cited by 311 · see citing works

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

Ge, Y., Tian, T., Huang, S., Wan, F., Li, J., Li, S., Wang, X., Yang, H., Hong, L., Wu, N., et al. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00568-6 · Cited by 300 · see citing works

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

Yang, Z., Bogdan, P., & Nazarian, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-81749-9 · Cited by 271 · see citing works

Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning

Zeng, X., Song, X. H., Ma, T., Pan, X., Zhou, Y., Hou, Y., Zhang, Z., Li, K., Karypis, G., & Cheng, F. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00316 · Cited by 258 · see citing works

Improved and Promising Identification of Human MicroRNAs by Incorporating a High-Quality Negative Set

Wei, L., Liao, M., Gao, Y., et al. (2013) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.146 · Cited by 241 · see citing works

A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing

Pham, T.-H., Qiu, Y., Zeng, J., Xie, L., & Zhang, P. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00285-9 · Cited by 219 · see citing works

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation

Hasan, M. M., Schaduangrat, N., Basith, S., Lee, G., Shoombuatong, W., & Manavalan, B. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa160 · Cited by 218 · see citing works

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov

Zhang, H., Saravanan, K. M., Yang, Y., Hossain, M. T., Li, J., Ren, X., Pan, Y. F., & Wei, Y. (2020) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00376-6 · Cited by 194 · see citing works

Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators

Yang, H. Q., Luo, Y., Ren, X., Wu, M., He, X., Peng, B., Deng, K., Yan, D., Tang, H., & Lin, H. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.015 · Cited by 192 · see citing works

BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides

Charoenkwan, P., Nantasenamat, C., Hasan, M. M., Manavalan, B., & Shoombuatong, W. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab133 · Cited by 181 · see citing works

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

Wei, L., Chen, H., & Su, R. (2018) · Molecular Therapy Nucleic Acids · doi:10.1016/j.omtn.2018.07.004 · Cited by 179 · see citing works

Emerging Pandemic Diseases: How We Got to COVID-19

Morens, D. M., & Fauci, A. S. (2020) · Cell · doi:10.1016/j.cell.2020.10.022 · Cited by 157 · see citing works

AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

Tang, B., He, F., Liu, D., He, F., Wu, T., Fang, M., Niu, Z., Wu, Z., & Xu, D. H. (2022) · Biomolecules · doi:10.3390/biom12060746 · Cited by 144 · see citing works

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides

Charoenkwan, P., Chiangjong, W., Nantasenamat, C., Hasan, M. M., Manavalan, B., & Shoombuatong, W. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab172 · Cited by 134 · see citing works

Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms

Wei, L., Hu, J., Li, F., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby107 · Cited by 128 · see citing works

DeepLGP: a novel deep learning method for prioritizing lncRNA target genes

Zhao, T., Hu, Y., Peng, J., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa428 · Cited by 127 · see citing works

<i>De Novo</i> Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence

Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0262 · Cited by 118 · see citing works

Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools

Prachař, M., Justesen, S. F. L., Steen-Jensen, D. B., Thorgrimsen, S., Jurgons, E., Winther, O., & Bagger, F. O. (2020) · Scientific Reports · doi:10.1038/s41598-020-77466-4 · Cited by 115 · see citing works

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

Morselli Gysi, D., do Valle, Í., Zitnik, M., Ameli, A., Gan, X., Varol, O., Ghiassian, S. D., Patten, J. J., Davey, R. A., Loscalzo, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025581118 · Cited by 107 · see citing works

Multi-substrate selectivity based on key loops and non-homologous domains: new insight into ALKBH family

Xu, B., Liu, D., Wang, Z., Tian, R., & Zuo, Y. (2020) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-020-03594-9 · Cited by 103 · see citing works

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

Batra, R., Chan, H., Kamath, G., Ramprasad, R., Cherukara, M. J., & Sankaranarayanan, S. K. R. S. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02278 · Cited by 101 · see citing works

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning

Hasan, M. M., Alam, M. A., Shoombuatong, W., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab167 · Cited by 97 · see citing works

Potential T-cell and B-cell Epitopes of 2019-nCoVPreprint

Fast, E., Altman, R. B., & Chen, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.19.955484 · Cited by 90 · see citing works

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking

Mohapatra, S., Nath, P., Chatterjee, M., Das, N., Kalita, D., Roy, P., & Satapathi, S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241543 · Cited by 77 · see citing works

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative ModelsPreprint

Chenthamarakshan, V., Das, P., Hoffman, S. C., Strobelt, H., Padhi, I., Lim, K. W., Hoover, B., Manica, M., Born, J., Laino, T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01215 · Cited by 73 · see citing works

Potentially highly potent drugs for 2019-nCoVPreprint

Nguyen, D. D., Gao, K., Chen, J., Wang, R., & Wei, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.05.936013 · Cited by 61 · see citing works

Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing

Belyaeva, A., Cammarata, L. V., Radhakrishnan, A., Squires, C., Yang, K., Shivashankar, G. V., & Uhler, C. (2021) · Nature Communications · doi:10.1038/s41467-021-21056-z · Cited by 60 · see citing works

A Machine Learning-based Diagnosis of Thyroid Cancer Using Thyroid Nodules Ultrasound Images

Ma, X., Xi, B., Zhang, Y., et al. (2019) · Current Bioinformatics · doi:10.2174/1574893614666191017091959 · Cited by 56 · see citing works

A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2

Wang, S., Sun, Q., Xu, Y., Pei, J., & Lai, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab211 · Cited by 52 · see citing works

Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

Karki, N., Verma, N., Trozzi, F., Tao, P., Kraka, E., & Zoltowski, B. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041573 · Cited by 43 · see citing works

Artificial Intelligence-Guided De Novo Molecular Design Targeting COVID-19

Srinivasan, S., Batra, R., Chan, H., Kamath, G., Cherukara, M. J., & Sankaranarayanan, S. K. R. S. (2021) · ACS Omega · doi:10.1021/acsomega.1c00477 · Cited by 42 · see citing works

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Liang, P., Yang, W., Chen, X., Long, C., Zheng, L., Li, H., & Zuo, Y. (2020) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2020.02.004 · Cited by 41 · see citing works

eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition

Wang, H., Liang, P., Zheng, L., Long, C., Li, H., & Zuo, Y. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab071 · Cited by 40 · see citing works

Integrated biomarker profiling of the metabolome associated with impaired fasting glucose and type 2 diabetes mellitus in large‐scale Chinese patients

Long, J., Yang, H. Q., Yang, Z., Jia, Q., Liu, L., Kong, L., Cui, H., Ding, S., Qin, Q., Zhang, N., et al. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.432 · Cited by 40 · see citing works

Single cell RNA and immune repertoire profiling of COVID-19 patients reveal novel neutralizing antibody

Li, F., Luo, M., Zhou, W., et al. (2020) · Protein & Cell · doi:10.1007/s13238-020-00807-6 · Cited by 39 · see citing works

Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

Che, M., Yao, K., Che, C., Cao, Z., & Kong, F. (2021) · Future Internet · doi:10.3390/fi13010013 · Cited by 39 · see citing works

Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2

Cheng, L., Han, X., Zhu, Z., Qi, C., Wang, P., & Zhang, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab042 · Cited by 39 · see citing works

Predicting Drug-target Interactions via FM-DNN Learning

Wang, J., Wang, H., Wang, X., et al. (2019) · Current Bioinformatics · doi:10.2174/1574893614666190227160538 · Cited by 36 · see citing works

Modular arrangements of sequence motifs determine the functional diversity of KDM proteins

Wang, Z., Liu, D., Xu, B., Tian, R., & Zuo, Y. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa215 · Cited by 27 · see citing works

An artificial intelligence system reveals liquiritin inhibits SARS-CoV-2 by mimicking type I interferonPreprint

Zhu, J., Deng, Y., Wang, X., Li, X.-F., Zhang, N., Liu, Z., Zhang, B., Qin, C., & Xie, Z. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.02.074021 · Cited by 26 · see citing works

Cancer Diagnosis and Disease Gene Identification via Statistical Machine Learning

Chen, L., Li, J., & Chang, M. (2020) · Current Bioinformatics · doi:10.2174/1574893615666200207094947 · Cited by 25 · see citing works

Identification of Novel Key Targets and Candidate Drugs in Oral Squamous Cell Carcinoma

Liu, J., Lian, X., Liu, F., et al. (2019) · Current Bioinformatics · doi:10.2174/1574893614666191127101836 · Cited by 24 · see citing works

Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model

Majumdar, S., Nandi, S. K., Ghosal, S., et al. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09840-x · Cited by 23 · see citing works

A Drug Target Interaction Prediction Based on LINE-RF Learning

Wang, J., Shi, Y., Wang, X., & Chang, H. (2019) · Current Bioinformatics · doi:10.2174/1574893615666191227092453 · Cited by 22 · see citing works

A computational framework for identifying the transcription factors involved in enhancer-promoter loop formation

Liu, L., Zhang, L., Dao, F., Yang, Y., & Lin, H. (2020) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2020.11.011 · Cited by 21 · see citing works

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.Preprint

Hsieh, K., Wang, Y., Chen, L., Zhao, Z., Savitz, S. I., Jiang, X., Tang, J. P., & Kim, Y. (2022) · PubMed · Cited by 21 · see citing works

COVID-19 induces lower levels of IL-8, IL-10, and MCP-1 than other acute CRS-inducing diseases

Cheng, L., Zhu, Z., Wang, C., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102960118 · Cited by 21 · see citing works

Research on Gastric Cancer’s Drug-resistant Gene Regulatory Network Model

Li, Z., Zhang, T., Lei, H., et al. (2019) · Current Bioinformatics · doi:10.2174/1574893614666190722102557 · Cited by 18 · see citing works

Revealing Gene Function and Transcription Relationship by Reconstructing Gene-Level Chromatin Interaction

Liu, L., Li, Q., Jin, W. Y., Lv, H., & Lin, H. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.01.011 · Cited by 17 · see citing works

Feasibility of Mesenchymal Stem Cell Therapy for COVID-19: A Mini Review

Li, J., Wang, X., Li, N., et al. (2020) · Current Gene Therapy · doi:10.2174/1566523220999200820172829 · Cited by 17 · see citing works

Artificial Intelligence for Bioinformatics and Biomedicine

Zhang, Y. (2020) · Current Bioinformatics · doi:10.2174/157489361508201221092330 · Cited by 8 · see citing works

Optimization in the Design of Natural Structures, Biomaterials, Bioinformatics and Biometric Techniques for Solving Physiological Needs and Ultimate Performance of Bio-devices

Wong, K. K. L. (2019) · Current Bioinformatics · doi:10.2174/157489361405190628122355 · Cited by 7 · see citing works

Transcription-based drug repurposing for COVID-19

Killick, R., Ballard, C. G., Doherty, P., et al. (2020) · Virus Research · doi:10.1016/j.virusres.2020.198176 · Cited by 7 · see citing works

A Mini-review of Computational Approaches to Predict Functions and Findings of Novel Micro Peptides

Nasir, M. A., Nawaz, S., & Huang, J. H. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200811130522 · Cited by 2 · see citing works

RGDtrip: A Database for the Investigation of Proteins Containing the RGD Tripeptide

Hatzis, Y., Thireou, T. N., Viennas, E. S., et al. (2017) · Current Bioinformatics · doi:10.2174/1574893612666170711153356 · Cited by 1 · see citing works

The role of AI in drug discovery: challenges, opportunities, and strategies

Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works

Confirmation of SARS-CoV-2 airborne dissemination indoors using “COVID-19 traps”

Orenes‐Piñero, E., Navas-Carrillo, D., Moreno‐Docón, A., Ortega-García, J. A., Torres-Cantero, A. M., García‐Vázquez, E., & Ramírez, P. (2021) · Journal of Infection · doi:10.1016/j.jinf.2021.12.017 · Cited by 4 · see citing works

And now for something completely different: from 2019-nCoV and COVID-19 to 2020-nMan

Froes, F. (2020) · Pulmonology · doi:10.1016/j.pulmoe.2020.02.010 · Cited by 25 · see citing works

ОПЫТ ИССЛЕДОВАНИЯ СЕРОПРЕВАЛЕНТНОСТИ К ВИРУСУ SARS-CoV-2 НАСЕЛЕНИЯ ИРКУТСКОЙ ОБЛАСТИ В ПЕРИОД ВСПЫШКИ COVID-19Preprint

Popova, A. Y., Ezhlova, E. B., Melnikova, A. A., Балахонов, С. В., Chesnokova, M. V., Dubrovina, V. I., Лялина, Л., Смирнов, В. С., Ag, T., Николаевич, П. А., et al. (2020) · doi:10.21055/preprints-3111809 · Cited by 2 · see citing works

Detection of SARS-CoV-2 in the ocular surface in different phases of COVID-19 patients in Shanghai, China

Li, M., Yang, Y., He, T., et al. (2021) · Annals of Translational Medicine · doi:10.21037/atm-20-6026 · Cited by 12 · see citing works

ОПЫТ ОЦЕНКИ ПОПУЛЯЦИОНОГО ИММУНИТЕТА К SARS-CoV-2 СРЕДИ НАСЕЛЕНИЯ ЛЕНИНГРАДСКОЙ ОБЛАСТИ В ПЕРИОД ЭПИДЕМИИ COVID-19Preprint

Popova, A. Y., Ezhlova, E. B., Melnikova, A. A., Istorik, O. A., Mosevich, O. S., Лялина, Л. В., Смирнов, В. С., Черный, М., Балабашыва, Н., Loginova, I. S., et al. (2020) · doi:10.21055/preprints-3111753 · Cited by 7 · see citing works