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Identification of captopril drug as a repurposable therapeutic candidate for COVID-19 treatment

Anisha Thakur¹, Robin Sinha¹, Preeti P.¹, Trapti Sharma¹, Kamal Rawal^#1

1. Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India #Corresponding Author
   Email ID: kamal.rawal@gmail.com
   Centre for Computational Biology and Bioinformatics,
   AIB Amity University, Noida

Website: http://drugx.kamalrawal.in/drugx/

Abstract

Background: Due to the COVID-19 pandemic more than 265 million people are infected and more than 5 million deaths across the world. A new variant of SARS-CoV-2, B.1.1.529 (Omicron), and on November 26 WHO designated B.1.1.529 as a variant of concern. The search for an effective and appropriate drug for the treatment of COVID-19 is still a big challenge. Due to the pandemic, various clinical trials are being conducted around the world to evaluate the existing drugs that were developed for other indications for efficacy and safety in COVID-19. In this study, we investigate whether a captopril drug is repurposed for COVID-19. The first angiotensin-converting enzyme (ACE) inhibitor is captopril. To cure heart failure and high blood pressure, captopril is used alone or in combination with other drugs. The COVID-19 S protein binds strongly to the angiotensin-converting-enzyme 2 (ACE2) receptor, an enzyme that physiologically counteracts renin-angiotensin-aldosterone system activity while also acting as a receptor for the COVID-19 virus. Drug repurposing has the potential to quickly identify antiviral therapeutics for the novel coronavirus disease.

Methods: Using machine learning approaches, We have implemented a multi-modal pipeline (COV-DrugX), containing 14 modules that were constructed using different approaches like chemical information, target-based, docking-based, symptom-based, target-based, and circuit-based to check whether a drug is repurposed for COVID-19. Here, we describe the effectiveness of the captopril drug for repurposing in COVID-19 based on the analysis of modules of the CoV-DrugX pipeline.

Results and conclusion: We reported that the captopril drug had similar features to COVID-19 based on deep learning modules that utilize chemoinformatics properties. The drug captopril also showed the interaction with COVID-19 targets, and it reported similar symptoms as COVID-19. The study concludes that captopril regulates UP/DOWN gene expression of the ACE2 gene. The CoV-DrugX pipeline gave a SI score of 8 (sum of all categorical values of all modules) and a Pi score of 0.62 (total executed tools run/SI score) to the captopril drug. Out of 14 modules, captopril obtained a score of 0 in 6 modules and 1 in 8 modules (100%). The captopril drug predicts a high score indicating its repurposing properties for COVID-19.

Keywords: Bioinformatics, Captopril, Artificial intelligence, COVID-19, drug repurposing, deep learning

1. Introduction

Coronavirus disease 2019 (COVID-19) emerged in November 2019, causing a global pandemic that resulted in medical problems and deaths, as well as having an impact on the global economy and everyday life. However, at present, a lack of specialized drugs to prevent or treat disease is a critical requirement [Prajapat et al., 2020]. The coronavirus causes pneumonia, colds, sneezing, and coughing in humans, while it causes diarrhea and upper respiratory diseases in animals. The corona virus spreads from one person to another person or animal to animal by air droplets. The coronavirus enters human cells via the ACE-2 exopeptidase receptor on the cell membrane [Kumar et. al., 2020].

Captopril is an ACE inhibitor that has been extensively studied for the treatment of mild to moderate essential hypertension, coronary artery disease, diabetes, and migraines.[Brogden et al., 1988]. Captopril was initially developed as a highly specific enzyme inhibitor; this primary action resulted in antihypertensive activity [Cushman et al.,1991]. Captopril's benefits in heart failure and hypertension are mostly due to suppression of the renin-angiotensin-aldosterone pathway (RAAS) [Gan et al., 2018]. It is an ACE inhibitor that inhibits ACE, which converts angiotensin I to angiotensin II. Angiotensin II binds to AT1 receptors on smooth muscles, affecting precapillary arterioles and postcapillary venules to contract, norepinephrine absorption to be inhibited, and catecholamine secreted from the adrenal medulla, all of which increase blood pressure. In the adrenal cortex, angiotensin II stimulates aldosterone secretion. Aldosterone induces the kidney's distal tubules and collects the duct to reabsorb water and sodium in exchange for potassium, causing expansion in extracellular volume and an increase in blood pressure [Lezama-martinez et al., 2018]. When ACE is inhibited it reduces plasma angiotensin II that leads to vasodilation (widening of blood vessels) and decreased aldosterone secretion. A decrease in aldosterone secretion can cause small increases in serum potassium, as well as sodium and fluid loss [Chen YJ et. al.,2018]. When captopril is given to hypertensive individuals, it lowers their peripheral vascular resistance. ACE inhibitors lower preload by causing vasodilation and natriuresis and reduce afterload by inhibiting the synthesis of angiotensin II in the cardiovascular system. An increase in cardiac output and a decrease in blood pressure is the overall effects [Herman LL et. al., 2021].

The discovery that SARS-CoV-2 and SARS-CoV use the ACE2 receptor for cell entry has significant implications for the pathophysiology of SARS-CoV-2. The spike protein interacts with ACE2 and SARS-CoV-2 infection resulting in downregulation of the ACE2 receptor but not ACE. This allows the virus to enter and replicate, as well as cause serious lung damage. The transmembrane protease serine 2 (TMPRSS2) inhibitor blocks the priming of the spike protein. The surface ACE2 receptor and a soluble form of ACE2 block by an anti-ACE2 antibody or peptides that competitively bind with SARS-CoV-2 and slow the viral entry into cells thus decreasing the spread of the virus. While it also protects the lungs from injury by its various enzymatic functions (Figure1) [Zhang et al., 2020].

The etiologic agent of the global COVID-19 pandemic is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The most common cause of death due to COVID-19 is the refractory acute respiratory distress syndrome (ARDS) caused by SARS-CoV-2 pneumonia. Numerous studies reveal that COVID-19 progression may be influenced by cardiovascular disease [Gao et.al., 2021]. Because captopril is an effective medicine that is also available in a liquid form, nebulization may be relevant for increasing lung activity while avoiding systemic side effects. To avoid ARDS, such a therapy could be employed for "Covid-19" patients with pneumonia. A SARS-CoV-2 virus invades the host via ACE2 receptors by decreasing the host's ACE2 expression. The dynamic equilibrium of the ACE/Ang II/AT1R axis and the ACE2/Ang (1–7)/Mas receptor axis is disrupted as a result of this. As a result, clinically approved ACE inhibitors such as captopril were investigated for their ability to activate ACE2 in a variety of medical conditions such as hypertension, inflammation, cardiovascular, renal, and lung diseases. These clinically approved drugs were discovered to activate ACE2, which was found to be downregulated in a variety of medical conditions such as hypertension, inflammation, cardiovascular, renal, and lung diseases. As a result, these drugs could be repurposed to reactivate COVID-19 patients' downregulated ACE2 [Chatterjee et al., 2020].

Drug repurposing or drug repositioning is a popular method of drug development since it reuses old drugs to medicate new diseases. Repurposing can help develop new medications for diseases at a lower cost and in less time, particularly when preclinical safety studies have already been completed [Dotolo et al., 2021]. Vaccines and antiviral treatments created from scratch for SARS-CoV-2 are expected to take at least 12–18 months to reach the clinic. The known pharmacological medicines with existing safety data may be repurposed much more quickly, and this is a significant focus of recent research [Farne et al., 2020]. Previously, we have developed various machine learning and bioinformatics platforms. These include text mining and network biology-based systems [Jagannadham et al., 2016], vaccine discovery system [Rawal et al. 2021], next-generation sequencing analysis system for cancer and other genomes [Preeti et al.,2021, Rawal et al 2011, Mandal et al 2006]. In this study to predict whether a captopril drug is COVID- 19 repurposed we used the CoV-DrugX pipeline.

2. Implementation

The CoV-DrugX pipeline (http://drugx.kamalrawal.in/drugx/) includes various modules based on different computational drug-repositioning approaches utilizing distinct sources from the distinct databases and literature based-text mining approaches for COVID 19. The CoV-DrugX pipeline indicates the suitability of drug repurposing in the COVID 19 situation. As an input, the CoV-Drug pipeline employs the SMILE of the drug. In this model, 14 modules analyze the properties of the given drug. Based on these analyses, this model predicts whether the given drug can be repurposed for COVID-19. This server by using the deep learning method checks whether a given drug is a COVID-19 repurpose drug by using 11 properties and 200 properties, has cured any COVID-19 condition, has shown any COVID-19 phenotype, side effect, interaction with any COVID-19 target, abnormal human gene expression during COVID-19, response with SARS-CoV-2 pathway circuit, docking with SARS-CoV-2 targets, docking with human targets targeted by SARS-CoV-2, docking with knowledge graph targets, has shown any, Check the euclidean distance between drug and SARS-CoV-2 disease, the drug shown interaction with AAK1, GAK, JAK2, and ACE2 and Check whether a given drug shown any COVID-19 symptom. These modules provide results in the form of scores 0 and 1 to the query drug.

2.1 Drug Feature Module:

The Drug_dl_11 module built using a deep learning approach considers a dataset of 262 drugs related to COVID-19 extracted using literature search and studied 11 biological properties related to COVID-19.

Drug_dl_200 module works on a similar dataset studying 200 chemoinformatics properties related to COVID-19 extracted from RDkit (https://rdkit.org/).

2.2 Docking module:

Docking-based drug-repositioning approaches are employed in the drug_dock_human, drug_dock_viral, and drug_dock_KG modules. In the drug dock viral module, we have included 23 viral proteins from SARS-COV-2 for docking. It involves the spike protein, membrane protein, envelop small membrane protein, nucleocapsid protein, main protease, papain-like protease, nsp3 (207–379), RNA-dependent RNA polymerase (RdRp, nsp12/7/8 complex), nsp7, nsp8, nsp12, helicase, nsp14, nsp15 (endoribonuclease), nsp10, nsp16, nsp 16/10 (2′-O-methyltransferase), nsp1, nsp2, nsp4, nsp6, nsp9, ORF3A, ORF6, ORF7A, ORF8, and ORF10. Also, since ACE2 and TMPRSS from humans have been considered to be important targets for viral entry, we included them for docking against the query drugs in drug_dock_human. Similarly, we have incorporated human protein-interacting protein kinases in drug_dock_KG which are AAK1, GAK, and JAK1/2, and it has been indicated that they have a function in viral endocytosis.

2.3 Side effect module:

The dataset for the Drug_side_effect module was created using the Sider (http://sideeffects.embl.de/) and OFFSIDES (http://tatonettilab.org/offsides/) databases. We've assembled the 6,123 drug adverse effects associated with the COVID-19 in the drug side effect module. In addition, we compiled a list of 3,052 drugs' distinct adverse effects. Based on drug-side effect associations, this module predicts whether or not a drug is relevant with COVID-19. If the module detects a side effect in the COVID-19 dataset, it predicts a score of 1; otherwise, it predicts a score of 0.

2.4 Target Module:

The Drug_target module is based upon the TTD database (http://db.idrblab.net/ttd/), which enlists 31,359 drugs and their targets. Also accumulates 378 targets related to covid 19 in a separate file based on literature search. The targets were collected using the drugs displaying some functional role in the treatment of COVID-19 (positive drug dataset). The working of the module is such that it gives the target for the query drug if it is associated with COVID-19. The module predicts a score of 1 if the target name was found in the COVID-19 dataset, which was extracted from the TTD dataset, else it predicts a score of 0.

2.5 Circuit module:

The DGIdb database (https://www.dgidb.org/downloads) was employed to extract information on 100,274 genes and their associated drugs for the Drug circuit module. Also, we have a total of 299 circuits and their associated gene-protein extracted from the research paper Loucera et al., 2020. The functioning of this module is such that it provides the circuit for an input query drug that is associated with COVID-19. Users can enter either the drug names (separated by a pipe in a text file) or the drug's SMILE notation (separated by newline characters in a text file). The module would take drug names as a query and scan the datasets for their associated interactions (genes) and circuit information. The module picks the gene associated with the provided drug and further checks on the associated circuit with the gene and its functional information such as host-virus interaction, inflammatory response, immune activity, antiviral defense, endocytosis, replication, and energetics. If the module has an associated circuit to our query drug in the dataset, then the module predicts a score of 1, otherwise, it will give 0.

2.6 Phenotype Module:

For the Drug_phenotype module, the source data get from the Webmd database (https://www.kaggle.com/rohanharode07/webmd-drug-reviews-dataset). This dataset contains 6,147 genes, their associated drugs. The dataset also contains the phenotype that is observed in the condition when the drug is prescribed. We have also extracted a total of 2,009 phenotypes observed in COVID-19, extracted using a literature search. The working of this module is such that it provides the phenotype of the input drug if it is associated with the COVID-19 and also provides several matching phenotypes of the drug with that of COVID-19.

2.7 Gene Expression Module:

In the Drug Gene expression module, data is derived from multiple sources such as DGIdb, the drug_central database, where the list of drugs and their interacting genes is derived from the DGIdb database, and drug-targetWebMD interaction data from the drug_central database. The COVID-19 Gene Expression processed file contains information about the gene symbol and its gene expression-related information including whether the gene is upregulated or downregulated by the drug. The module would provide information about genes associated with the drug and expression information for these genes, whether they are upregulated or downregulated. A gene associated with the query drug is searched in the database and the drugs that interact with COVID-19 genes are assigned a score of 1. If in the DECs database the gene is not found then the query drug is assigned a score of 0, i.e., the query drug doesn’t interact with any gene associated with COVID-19.

3. Usage

Captopril is a potent, competitive inhibitor of the ACE. The 3D structure of captopril was downloaded from the PubChem (https://pubchem.ncbi.nlm.nih.gov/compound/Captopril) see figure 2(Supplementary 2). The DrugBank ID of the captopril drug is ‘DB01197’. The CoV-DrugX pipeline takes the SMILE of the drug as an input file. The SMILE of captopril is “C[C@H](CS)C(=O)N1CCC[C@H]1C(O)=O” and was retrieved from the drug bank (https://go.drugbank.com/). In the CoV-DrugX pipeline, upload the file containing the SMILE of the drug. We can study a single property in this pipeline by selecting one module at a time, or we can analyze all properties at once by selecting all modules. In this study we selected all 14 modules at one time named as drug-circuit, drug-target, drug_dock_human, drug_dock_viral, drug_dock_KG, drug_phenotype, drug_AI_ranking, drug_condition, drug_side_effet, drug_side_effect_neighbours, drug_gene_expression, drug_dl_11, drug_dl_200 and drug_gene_network (Supplementary 2 Figure3). After submitting the file the post submission page will appear which contains the details of the query including the Job ID, result status, and other details. The Result of the query appears within minutes. The resulting file shows the score for each module, SI score, and Pi score. The SI score is the Sum of all categorical values of all tools from A to N and the Pi score is the total executed tools run/ Si score. The resulting file can be downloaded in CSV format. The scores for each module are produced in the range 0 and 1 where 0 refers to the drug not to be considered in drug repurposing for COVID-19, while 1 symbolizes that the drug should be considered in COVID-19 drug repurposing. In addition, intermediate files are generated for each module, containing additional functional and detailed information about the input drug.

4. Result and Discussion

The CoV-DrugX pipeline’s modules are based on a variety of drug repurposing-based computational approaches. There are 14 distinct modules in the CoV-DrugX pipeline. Two modules, Drug_dl_11 and Drug_dl_200, use the deep learning method. These modules are based on the 11 biological properties and 200 chemoinformatics properties of the SARS COVID-19 virus, respectively. These modules predict whether the drug is a COVID-19 repurposable drug or not. There are a set of other modules that analyze the drugs based on conditions related to COVID 19, phenotypes observed in COVID 19, drugs showing symptoms similar to COVID 19, side effects of the drugs related to curing, and preventing COVID 19, and a target gene expression-based approach as well. The other modules in this pipeline are docking-based. The docking-based modules are the human target-based docking module, the second is the viral protein target-based module, and the third is protein kinases associated with human proteins as targets for the query drugs. The Euclidean distance between a drug and the COV-2 disease is calculated using the CoV-DrugX AI ranking module. The Pathway Circuit Module checks the query drug if it has any association with the SARS COV-2 circuit.

The DL_11 module uses the deep learning approach and for the captopril drug, it predicts the 0 value score (Supplementary 1 Table 1). A DL_200, deep learning module of CoV-DrugX predicts the captopril medication to be a potential drug based on 200 chemoinformatics properties and a reported score of 1(Supplementary 1 Table 2).

The module Drug_Condition predicts that there is only 1 condition for the drug is in the CoV-DrugX database and there are 34 COVID-19 conditions in total. It indicates a 0 score for the COVID-19 condition for the captopril drug and an overall 0 score due to a lack of information in the database. (Supplementary 1 Table3). The module CoV-DrugX Phenotype for captopril drug predicts that there are 8 phenotypes available for drugs, and all eight are COVID-19 phenotypes in the captopril drug. This shows the 1.0 similarity and the Drugx value is 1. The COVID-19 phenotypes in drugs are Maternal Virilization, Acne or Hirsutism, Fetal Virilization-Prader Iv, Normal Ovarian, Bone Age-Delay, Spontaneous Breast Development In Puberty, Androgenic Signs In Puberty, Normal OGTT (Supplementary 1 Table 4).

The analysis for COVID-19 side effects of captopril shows that there are a total of 2977 side effects available for captopril, and we found that there are a total of 358 COVID-19 side effects in captopril drug. (Supplementary 1:Table 5).

In Drug_target_module captopril has shown an interaction with the COVID-19 drug target ANGIOTENSIN-CONVERTING ENZYME (ACE) (Supplementary 1 Table6). In the human_gene_expression module, captopril drug results show that the expression of MMP2, PIK3CG, CYP19A1, MMP9, BDKRB1, GNRH1, and AGTR1 is UP regulated during COVID-19. The expression of MYO9B and MMP12 genes are down-regulated. Some genes show both UP and DOWN regulation, and these genes are PTGS1, ACE, PTGS2, DPP4, SST, EHMT2, SERPINE1, ACE2, NOS2, and REN (Supplementary 1 Table7). This study predicts that captopril will show an interaction with the COVID-19 drug target ANGIOTENSIN-CONVERTING ENZYME (ACE). SARS-CoV-2 uses the ACE2 as the binding receptor to enter the cell. Results show that captopril UP/DOWN regulates the ACE2 gene. Unbalanced levels of the number of MMPs have been reported in lung diseases such as COPD, asthma, pulmonary fibrosis, and ALI/ARDS, as well as in the inflammatory process associated with COVID-19 [Hardy & Fernandez-patron, 2021]. Captopril drug UP regulates the expression of MMP2 and MMP9 genes and down-regulates the expression of the MMP12 gene.

When a captopril drug is docked against 23 viral proteins using Drug_Dock_Viral, the results are interpreted in the form of binding affinities, which show the strength of binding interaction. The mean affinity is -4.78 KCal/mol, the median affinity is -4.7KCal/mol, and the STD affinity is 0.8 KCal/mol (Supplementary 1 Table 8). The bar chart showing the distribution of binding affinities for viral proteins is shown in Figure 4 (Supplementary 2).

The docking against human proteins ACE2 and TMPRSS2 predicts that captopril binding affinity for ACE2 is -5.1 and for TMPRSS2 is -4.4. Table 9 in Supplementary 1. The module docking with knowledge graph targets shows the protein kinases AAK1, GAK, and JAK12 are associated with human proteins ACE2 and TMPRSS2. We find that binding affinity for AAK1, GAK, and JAK2 is -5.1, -5.4, and -6 respectively (Supplementary 1 Table10). A histogram plot to further analyze the binding affinity of human proteins and related protein kinases is shown in Figure 5 (Supplementary 2).

The CoV-DrugX circuit Pathway Module predicts that the MMP9 of Hepatitis B does not show any host-virus interaction, inflammatory response, endocytosis, or replication energetics, immune activity, anti-viral defense. The SERPINE1 gene protein of the HIF-1 signaling pathway shows only the inflammatory response, antiviral defense, and endocytosis. NOS2 of the HIF-1 signaling pathway shows a 1 value for replication and 0 for other properties. In the tuberculosis circuit, NOS2 gene-protein is associated with replication (Supplementary 1 Table11). Captopril shows the highest euclidean distance of 12.95795 with model 3 (Supplementary 1 Table12). The result predicts that a total of 73 symptoms are available for captopril, of which a total of 10 symptoms are COVID-19 symptoms (Supplementary 1 Table 13).

For all the modules, we get scores in the form of 0 and 1. The 6 modules are reported with a score of 0, and they include DL_11, COVID-19 Drug_condition, drug_dock_human, drug_dock_viral, drug_dock_KG, and drug interaction with AAK1; GAK; JAK2; and ACE2 modules because of the lack of any evident information about the drug in the source databases associated with these modules. The other 8 modules reported a score of 1, and these include DL_200, drug_phenotype, drug_side effect, drug interaction with COVID-19 target, drug_gene expression, drug_pathway circuit, drug_AI ranking, and drug_COVID19 symptoms. These modules, which give a score of 1 recognize the drug to be considered as a COVID-19 repurposable drug. The compiled scores for all 14 modules are listed in Table 14 (Supplementary 1). For captopril, the SI score ( Sum of all categorical values of all modules) is 8 and the Pi score (total executed tools run/SI score) is 0.62. Out of 14 modules, captopril obtained a score of 0 in 6 modules and 1 in 8 modules (100%). Hence, based upon the average score, We get to the conclusion that there is a 100% probability of being considered for repurposing against COVID-19.

5. Conclusion

The importance of developing a COVID-19 vaccine, therapies, and diagnostics have resulted in enormous public and commercial investment in R&D. A significant portion of this effort has been focused on developing a global COVID-19 vaccination that is both safe and effective. Drug repurposing has various advantages including a considerable decrease in costs, reduces the time it takes to produce a drug since several Existing drugs have been proven to be safe in humans; it does not require Phase 1 clinical studies and it has the potential to be reused despite evidence of side effects and failed efficacy in some indications. The studies suggest captopril to be a possible cure for COVID-19 based on the results of the captopril drug against the DrugX database, which was extensively constructed for repurposing of COVID-19. Researchers were looking to identify a treatment that would be effective, cheap, and easy to use and captopril appears to meet all of these criteria.

6. Supplementary material

Supplementary 1-

The Supplementary file of Tables

Supplementary 2-

The Supplementary file of figures

7. Acknowledgment

Dr. Kamal Rawal acknowledges the support provided by SERB, Department of Science and Technology (Grant ID: CVD/2020/000842). The project involved the usage of computational infrastructure (server etc) provided by the Department of Biotechnology (DBT), Ministry of Science and Technology Government of India (Grant ID: BT/PRI7252/BID/7/708/2016) and Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA. We are also thankful to Amity University for the support provided during the conduct of this study.

8. Contribution of Authors

This study was conducted under the overall guidance of KR, who contributed to the protocol, critical evaluation of data, and manuscript. The pipeline was designed, constructed, and validated by RS and PS. Manuscript writing was done by Anisha Thakur.

9. Financial Support and Sponsorship

Dr. Kamal Rawal acknowledges the support provided by SERB, Department of Science and Technology (Grant ID: CVD/2020/000842). The project involved the usage of computational infrastructure (server etc) provided by the Department of Biotechnology (DBT), Ministry of Science and Technology Government of India (Grant ID: BT/PRI7252/BID/7/708/2016). SKN, PP, R, SS, SDS, NG, and TS have received financial support from grants obtained from Robert J. Kleberg Jr. and Helen C. Kleberg Foundation and Baylor College of Medicine, Houston, Texas, USA. We are also thankful to Amity University for the support provided during the conduct of this study.

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27. Gupta, R., Soni, Patnaik, Sood, I., Singh, R., Rawal, K., Rani, V. High AU content: A Signature of Upregulated miRNA in Cardiac Diseases, Bioinformation, vol. 3, pp.132-135, 2010

Vaccine Development

28. Abbasi, B. A., Saraf, D., Sharma, T., Sinha, R., Singh, S., Gupta, P. Rawal, K. (2020, April 8). Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. https://doi.org/10.31219/osf.io/f8zyw

29. Kamal Rawal, v, Abbasi, B. A., et al (2020). Design of a multi-epitope Chagas disease vaccine by computational analysis of the Trypanosoma cruzi CL Brenner proteome. (Communicated)

30. Rawal k, Sinha R et al (2020) Vaxi – DL: A web-based Deep Learning (DL) server to identify Potential Vaccine Candidates, (Bioinformatics- communicated)

COVID-19

31. Jethani B., Rawal, K. et al (2020). Clinical Characteristics and Remedy Profile of Patients with COVID-19: Retrospective Cohort Study, Accepted (In Press)

32. Rawal, K., Sinha R et al (2020) To Study the Effect of Unconventional Treatment Protocol on COVID-19 patients in Delhi using AI based techniques (Lancet- communicated)

Machine Learning

33. Rawal, K., Khurana T, Sharma H, . 2019. An extensive survey of molecular docking tools and their applications using text mining and deep curation strategies. PeerJ Preprints7:e27538v1 https://doi.org/10.7287/peerj.preprints.27538v1

34. Established the new framework/portal of "obesity treatment" and other diseases such as type 2 diabetes and hypertension among the people living sedentary lifestyle by engaging relevant sections of community using social networks, data science systems, machine learning, sensors and mobile apps. The system is being used by general public as a social service initiative.

35. Development of a Web Based Weight Loss Programme. K. Rawal, P. Gaur and K. Kashive, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, October, 2014

36. Developing Networks in Obesity using Text Mining. K. Rawal, S. Agarwal and J. Jagannadham, LAP LAMBERT Academic Publishing GmbH & Co. KG, Saarbrücken Germany, September, 2014.

37. Loucera, C., Esteban-Medina, M., Rian, K., Falco, M. M., Dopazo, J., & Peña-Chilet, M. (2020). Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection. Signal transduction and targeted therapy, 5(1), 1-3.

38. CoV-DrugX pipeline. https://osf.io/x2ky5/

39. Hardy E, Fernandez-Patron C. Targeting MMP-Regulation of Inflammation to Increase Metabolic Tolerance to COVID-19 Pathologies: A Hypothesis. Biomolecules. 2021 Mar;11(3):390.