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Bazzi-Allahri F, Shiri F, Ahmadi S, Toropova AP, Toropov AA. SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CL pro and RdRp viral proteins. BMC Chem 2024; 18:191. [PMID: 39363220 DOI: 10.1186/s13065-024-01302-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 09/18/2024] [Indexed: 10/05/2024] Open
Abstract
The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy.
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Affiliation(s)
| | | | - Shahin Ahmadi
- Department of Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Alla P Toropova
- Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
| | - Andrey A Toropov
- Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
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Ivanov JM, Tenchov R, Ralhan K, Iyer KA, Agarwal S, Zhou QA. In Silico Insights: QSAR Modeling of TBK1 Kinase Inhibitors for Enhanced Drug Discovery. J Chem Inf Model 2024. [PMID: 39289178 DOI: 10.1021/acs.jcim.4c00864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
TBK1, or TANK-binding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a crucial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and antitumor immunity. Dysregulation of TBK1 activity can lead to autoimmune diseases, neurodegenerative disorders, and cancer. Due to its central role in these critical pathways, TBK1 is a significant focus of research for therapeutic drug development. In this paper, we explore data from the CAS Content Collection regarding TBK1 and its implication in a large assortment of diseases and disorders. With the demand for developing efficient TBK1 inhibitors being outlined, we focus on utilizing a machine learning approach for developing predictive models for TBK1 inhibition, derived from the fragment-functional analysis descriptors. Using the extensive CAS Content Collection, we assembled a training set of TBK1 inhibitors with experimentally measured IC50 values. We explored several machine learning techniques combined with various molecular descriptors to derive and select the best TBK1 inhibitor QSAR models. Certain significant structural alerts that potentially contribute to inhibition of TBK1 are outlined and discussed. The merit of the article stems from identifying the most adequate TBK1 QSAR models and subsequent successful development of advanced positive training data to facilitate and enhance drug discovery for an important therapeutic target such as TBK1 inhibitors, based on an extensive, wide-ranging set of scientific information provided by the CAS Content Collection.
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Affiliation(s)
- Julian M Ivanov
- CAS, A Division of the American Chemical Society, Columbus, Ohio 43210, United States
| | - Rumiana Tenchov
- CAS, A Division of the American Chemical Society, Columbus, Ohio 43210, United States
| | | | - Kavita A Iyer
- ACS International India Pvt. Ltd., Pune 411044, India
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Luo X, Xu T, Ngan DK, Xia M, Zhao J, Sakamuru S, Simeonov A, Huang R. Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure. Toxicol Appl Pharmacol 2024; 492:117098. [PMID: 39251042 DOI: 10.1016/j.taap.2024.117098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 07/31/2024] [Accepted: 09/06/2024] [Indexed: 09/11/2024]
Abstract
Exposure to various chemicals found in the environment and in the context of drug development can cause acute toxicity. To provide an alternative to in vivo animal toxicity testing, the U.S. Tox21 consortium developed in vitro assays to test a library of approximately 10,000 drugs and environmental chemicals (Tox21 10 K compound library) in a quantitative high-throughput screening (qHTS) approach. In this study, we assessed the utility of Tox21 assay data in comparison with chemical structure information in predicting acute systemic toxicity. Prediction models were developed using four machine learning algorithms, namely Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine, and their performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC). The chemical structure-based models as well as the Tox21 assay data demonstrated good predictive power for acute toxicity, achieving AUC-ROC values ranging from 0.83 to 0.93 and 0.73 to 0.79, respectively. We applied the models to predict the acute toxicity potential of the compounds in the Tox21 10 K compound library, most of which were found to be non-toxic. In addition, we identified the Tox21 assays that contributed the most to acute toxicity prediction, such as acetylcholinesterase (AChE) inhibition and p53 induction. Chemical features including organophosphates and carbamates were also identified to be significantly associated with acute toxicity. In conclusion, this study underscores the utility of in vitro assay data in predicting acute toxicity.
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Affiliation(s)
- Xi Luo
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Tuan Xu
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Deborah K Ngan
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Menghang Xia
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Jinghua Zhao
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Srilatha Sakamuru
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Anton Simeonov
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA
| | - Ruili Huang
- Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA.
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Zhang Y, Tian Y, Yan A. A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:531-563. [PMID: 39077983 DOI: 10.1080/1062936x.2024.2375513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 06/27/2024] [Indexed: 07/31/2024]
Abstract
The 3C-like Proteinase (3CLpro) of novel coronaviruses is intricately linked to viral replication, making it a crucial target for antiviral agents. In this study, we employed two fingerprint descriptors (ECFP_4 and MACCS) to comprehensively characterize 889 compounds in our dataset. We constructed 24 classification models using machine learning algorithms, including Support Vector Machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost), and Deep Neural Networks (DNN). Among these models, the DNN- and ECFP_4-based Model 1D_2 achieved the most promising results, with a remarkable Matthews correlation coefficient (MCC) value of 0.796 in the 5-fold cross-validation and 0.722 on the test set. The application domains of the models were analysed using dSTD-PRO calculations. The collected 889 compounds were clustered by K-means algorithm, and the relationships between structural fragments and inhibitory activities of the highly active compounds were analysed for the 10 obtained subsets. In addition, based on 464 3CLpro inhibitors, 27 QSAR models were constructed using three machine learning algorithms with a minimum root mean square error (RMSE) of 0.509 on the test set. The applicability domains of the models and the structure-activity relationships responded from the descriptors were also analysed.
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Affiliation(s)
- Y Zhang
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - Y Tian
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
| | - A Yan
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, P. R. China
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Saquib Q, Bakheit AH, Ahmed S, Ansari SM, Al-Salem AM, Al-Khedhairy AA. Identification of Phytochemicals from Arabian Peninsula Medicinal Plants as Strong Binders to SARS-CoV-2 Proteases (3CL Pro and PL Pro) by Molecular Docking and Dynamic Simulation Studies. Molecules 2024; 29:998. [PMID: 38474509 DOI: 10.3390/molecules29050998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/04/2024] [Accepted: 02/14/2024] [Indexed: 03/14/2024] Open
Abstract
We provide promising computational (in silico) data on phytochemicals (compounds 1-10) from Arabian Peninsula medicinal plants as strong binders, targeting 3-chymotrypsin-like protease (3CLPro) and papain-like proteases (PLPro) of SARS-CoV-2. Compounds 1-10 followed the Lipinski rules of five (RO5) and ADMET analysis, exhibiting drug-like characters. Non-covalent (reversible) docking of compounds 1-10 demonstrated their binding with the catalytic dyad (CYS145 and HIS41) of 3CLPro and catalytic triad (CYS111, HIS272, and ASP286) of PLPro. Moreover, the implementation of the covalent (irreversible) docking protocol revealed that only compounds 7, 8, and 9 possess covalent warheads, which allowed the formation of the covalent bond with the catalytic dyad (CYS145) in 3CLPro and the catalytic triad (CYS111) in PLPro. Root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and radius of gyration (Rg) analysis from molecular dynamic (MD) simulations revealed that complexation between ligands (compounds 7, 8, and 9) and 3CLPro and PLPro was stable, and there was less deviation of ligands. Overall, the in silico data on the inherent properties of the above phytochemicals unravel the fact that they can act as reversible inhibitors for 3CLPro and PLPro. Moreover, compounds 7, 8, and 9 also showed their novel properties to inhibit dual targets by irreversible inhibition, indicating their effectiveness for possibly developing future drugs against SARS-CoV-2. Nonetheless, to confirm the theoretical findings here, the effectiveness of the above compounds as inhibitors of 3CLPro and PLPro warrants future investigations using suitable in vitro and in vivo tests.
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Affiliation(s)
- Quaiser Saquib
- Zoology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Ahmed H Bakheit
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Sarfaraz Ahmed
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
| | - Sabiha M Ansari
- Botany & Microbiology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdullah M Al-Salem
- Zoology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdulaziz A Al-Khedhairy
- Zoology Department, College of Sciences, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
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Güner E, Özkan Ö, Yalcin-Ozkat G, Ölgen S. Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods. Med Chem 2024; 20:153-231. [PMID: 37957860 DOI: 10.2174/0115734064265609231026063624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 08/30/2023] [Accepted: 09/25/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the deep learning method. METHODS For this purpose, a Trained Neural Network (TNN) was used to produce 75 molecules similar to Favipiravir by using Simplified Molecular Input Line Entry System (SMILES) representations. The binding properties of molecules to Viral RNA-dependent RNA polymerase (RdRp) were studied by using molecular docking studies. To confirm the accuracy of this method, compounds were also tested against 3CL protease (3CLpro), which is another important enzyme for the progression of SARS-CoV-2. Compounds having better binding energies and RMSD values than favipiravir were searched with similarity analysis on the ChEMBL drug database in order to find similar structures with RdRp and 3CLpro inhibitory activities. RESULTS A similarity search found new 200 potential RdRp and 3CLpro inhibitors structurally similar to produced molecules, and these compounds were again evaluated for their receptor interactions with molecular docking studies. Compounds showed better interaction with RdRp protease than 3CLpro. This result presented that artificial intelligence correctly produced structures similar to favipiravir that act more specifically as RdRp inhibitors. In addition, Lipinski's rules were applied to the molecules that showed the best interaction with RdRp, and 7 compounds were determined to be potential drug candidates. Among these compounds, a Molecular Dynamic simulation study was applied for ChEMBL ID:1193133 to better understand the existence and duration of the compound in the receptor site. CONCLUSION The results confirmed that the ChEMBL ID:1193133 compound showed good Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bonding, and remaining time in the active site; therefore, it was considered that it could be active against the virus. This compound was also tested for antiviral activity, and it was determined that it did not delay viral infection, although it was cytotoxic between 5mg/mL-1.25mg/mL concentrations. However, if other compounds could be tested, it might provide a chance to obtain activity, and compounds should also be tested against the enzymes as well as the other types of viruses.
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Affiliation(s)
- Ersin Güner
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Biruni University, 34010 Topkapı, İstanbul, Turkey
| | - Özgür Özkan
- Teknokent Arı, Pinticks Software Company, Istanbul Technical University, Reşitpaşa Mah. Katar Street, No:4/B204 Sarıyer, İstanbul, Turkey
| | - Gözde Yalcin-Ozkat
- Bioengineering Department, Faculty of Engineering and Architecture, Recep Tayyip Erdogan University, 53100 Rize, Turkey
- Max Planck Institute for Dynamics of Complex Technical Systems, Molecular Simulations and Design Group, Sandtorstrasse 1, 39106 Magdeburg, Germany
| | - Süreyya Ölgen
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Biruni University, 34010 Topkapı, İstanbul, Turkey
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Elkashlan M, Ahmad RM, Hajar M, Al Jasmi F, Corchado JM, Nasarudin NA, Mohamad MS. A review of SARS-CoV-2 drug repurposing: databases and machine learning models. Front Pharmacol 2023; 14:1182465. [PMID: 37601065 PMCID: PMC10436567 DOI: 10.3389/fphar.2023.1182465] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.
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Affiliation(s)
- Marim Elkashlan
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Malak Hajar
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Juan Manuel Corchado
- Departamento de Informática y Automática, Facultad de Ciencias, Grupo de Investigación BISITE, Instituto de Investigación Biomédica de Salamanca, University of Salamanca, Salamanca, Spain
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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Takada Y, Kaneko K. Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na +/K +-ATPase. Pharmacol Rep 2023:10.1007/s43440-023-00508-x. [PMID: 37354314 DOI: 10.1007/s43440-023-00508-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/09/2023] [Accepted: 06/16/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na+/K+-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments. METHODS The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities. RESULTS The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development. CONCLUSION The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.
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Affiliation(s)
- Yohei Takada
- Corporate Planning Department, Otsuka Holdings Co., Ltd, Shinagawa Grand Central Tower 2-16-4 Konan, Minato-ku, Tokyo, 108-8241, Japan.
| | - Kazuhiro Kaneko
- Headquarters of Clinical Development, Otsuka Pharmaceutical Co., Ltd, Shinagawa Grand Central Tower 2-16-4 Konan, Minato-ku, Tokyo, 108-8241, Japan
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Eawsakul K, Ongtanasup T, Ngamdokmai N, Bunluepuech K. Alpha-glucosidase inhibitory activities of astilbin contained in Bauhinia strychnifolia Craib. stems: an investigation by in silico and in vitro studies. BMC Complement Med Ther 2023; 23:25. [PMID: 36717857 PMCID: PMC9885589 DOI: 10.1186/s12906-023-03857-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 01/23/2023] [Indexed: 01/31/2023] Open
Abstract
INTRODUCTION Bioactive compounds from traditional medicines are good alternatives to standard diabetes therapies and may lead to new therapeutic discoveries. The stems of Bauhinia strychnifolia Craib. (BC) have a possible antihyperglycemic effect; However, the extraction of astilbin from BC has never been recorded in alpha-glucosidase inhibitory activities. METHODS Using liquid chromatography-mass spectrometry (LC-MS/MS), 32 compounds were detected in the BC extract. The screening was based on peak area. Seven compounds found. PASS recognized all seven compounds as potential alpha-glucosidase (AG) inhibitors. Astilbin and quercetin 3-rhamnoside were the most likely inhibitors of AG. Arguslab, AutoDock, and AutoDock Vina investigated the binding of the two compounds and AG. The binding stability was confirmed by molecular dynamics (MD). In addition, the optimum solvent extraction was studied via CosmoQuick, and extracts were examined with 1H-NMR prior to testing with AG. RESULTS All three software programs demonstrated that both compounds inhibit AG more effectively than acarbose. According to the sigma profile, THF is recommended for astilbin extraction. The BC extract with THF showed outstanding AG inhibitory action with an IC50 of 158 ± 1.30 µg mL-1, which was much lower than that of the positive control acarbose (IC50 = 190 ± 6.97 µg mL-1). In addition, astilbin from BC was found to inhibit AG strongly, IC50 = 22.51 ± 0.70 µg mL-1 through the extraction method of large-scale astilbin with THF has the best extraction capacity compared to other solvents, hence the initial stage of extraction employs THF to extract and precipitate them with ethyl acetate and water. CONCLUSION In silico and in vitro studies reveal that astilbin inhibits AG and is superior to acarbose, validating its promise as an AG inhibitor. Overall, astilbin was the most bioactive component of BC for antidiabetic action.
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Affiliation(s)
- Komgrit Eawsakul
- grid.412867.e0000 0001 0043 6347 Department of Applied Thai Traditional Medicine, School of Medicine, Walailak University, Nakhon Si Thammarat, 80160 Thailand ,grid.412867.e0000 0001 0043 6347School of Allied Health Sciences and Research Excellence Center for Innovation and Health Products (RECIHP), Walailak University, Nakhon Si Thammarat, 80160 Thailand
| | - Tassanee Ongtanasup
- grid.412867.e0000 0001 0043 6347 Department of Applied Thai Traditional Medicine, School of Medicine, Walailak University, Nakhon Si Thammarat, 80160 Thailand
| | - Ngamrayu Ngamdokmai
- grid.412867.e0000 0001 0043 6347 Department of Applied Thai Traditional Medicine, School of Medicine, Walailak University, Nakhon Si Thammarat, 80160 Thailand
| | - Kingkan Bunluepuech
- grid.412867.e0000 0001 0043 6347 Department of Applied Thai Traditional Medicine, School of Medicine, Walailak University, Nakhon Si Thammarat, 80160 Thailand ,grid.412867.e0000 0001 0043 6347School of Allied Health Sciences and Research Excellence Center for Innovation and Health Products (RECIHP), Walailak University, Nakhon Si Thammarat, 80160 Thailand
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Mahmud ML, Islam S, Biswas S, Mortuza MG, Paul GK, Uddin MS, Akhtar-E-Ekram M, Saleh MA, Zaman S, Syed A, Elgorban AM, Zaghloul NSS. Klebsiella pneumoniae Volatile Organic Compounds (VOCs) Protect Artemia salina from Fish Pathogen Aeromonas sp.: A Combined In Vitro, In Vivo, and In Silico Approach. Microorganisms 2023; 11:microorganisms11010172. [PMID: 36677466 PMCID: PMC9862385 DOI: 10.3390/microorganisms11010172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/24/2022] [Accepted: 11/26/2022] [Indexed: 01/12/2023] Open
Abstract
Antibiotic resistance is an alarming threat all over the world, and the biofilm formation efficacy of bacteria is making the situation worse. The antagonistic efficacy of Klebsiella pneumoniae against one of the known fish pathogens, Aeromonas sp., is examined in this study. Moreover, Aeromonas sp.'s biofilm formation ability and in vivo pathogenicity on Artemia salina are also justified here. Firstly, six selected bacterial strains were used to obtain antimicrobial compounds against this pathogenic strain. Among those, Klebsiella pneumoniae, another pathogenic bacterium, surprisingly demonstrated remarkable antagonistic activity against Aeromonas sp. in both in vitro and in vivo assays. The biofilm distrusting potentiality of Klebsiella pneumoniae's cell-free supernatants (CFSs) was likewise found to be around 56%. Furthermore, the volatile compounds of Klebsiella pneumoniae were identified by GC-MS in order to explore compounds with antibacterial efficacy against Aeromonas sp. through an in silico study, where 5'-methylthioadenosine/S-adenosylhomocysteine nucleosidase (MTAN) (PDB: 5B7P) was chosen as a target protein for its unique characteristics and pathogenicity. Several volatile compounds, such as oxime- methoxy-phenyl-, fluoren-9-ol, 3,6-dimethoxy-9-(2-phenylethynyl)-, and 2H-indol-2-one, 1,3-dihydro- showed a strong binding affinity, with free energy of -6.7, -7.1, and -6.4 Kcal/mol, respectively, in complexes with the protein MTAN. Moreover, the root-mean-square deviation, solvent-accessible surface area, radius of gyration, root-mean-square fluctuations, and hydrogen bonds were used to ensure the binding stability of the docked complexes in the atomistic simulation. Thus, Klebsiella pneumoniae and its potential compounds can be employed as an alternative to antibiotics for aquaculture, demonstrating their effectiveness in suppressing Aeromonas sp.
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Affiliation(s)
- Md. Liton Mahmud
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Shirmin Islam
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Suvro Biswas
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md. Golam Mortuza
- Department of Science and Humanities, Bangladesh Army International University of Science and Technology, Cumilla 3500, Bangladesh
| | - Gobindo Kumar Paul
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
- Bangladesh Reference Institute for Chemical Measurements (BRICM), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka 1205, Bangladesh
| | - Md. Salah Uddin
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md. Akhtar-E-Ekram
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
| | - Md. Abu Saleh
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
- Correspondence: (M.A.S.); (S.Z.)
| | - Shahriar Zaman
- Microbiology Laboratory, Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi 6205, Bangladesh
- Correspondence: (M.A.S.); (S.Z.)
| | - Asad Syed
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Abdallah M. Elgorban
- Department of Botany and Microbiology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Nouf S. S. Zaghloul
- Bristol Centre for Functional Nanomaterials, HH Wills Physics Laboratory, Tyndall Avenue, University of Bristol, Bristol BS8 1FD, UK
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11
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Liang J, Zheng Y, Tong X, Yang N, Dai S. In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning. Molecules 2022; 28:208. [PMID: 36615401 PMCID: PMC9821958 DOI: 10.3390/molecules28010208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/23/2022] [Indexed: 12/28/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.
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Affiliation(s)
- Jihao Liang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Yang Zheng
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Xin Tong
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Naixue Yang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
| | - Shaoxing Dai
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, Kunming 650500, China
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12
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Progress on COVID-19 Chemotherapeutics Discovery and Novel Technology. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238257. [PMID: 36500347 PMCID: PMC9736643 DOI: 10.3390/molecules27238257] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/19/2022] [Accepted: 11/20/2022] [Indexed: 11/29/2022]
Abstract
COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel highly contagious and pathogenic coronavirus that emerged in late 2019. SARS-CoV-2 spreads primarily through virus-containing droplets and small particles of air pollution, which greatly increases the risk of inhaling these virus particles when people are in close proximity. COVID-19 is spreading across the world, and the COVID-19 pandemic poses a threat to human health and public safety. To date, there are no specific vaccines or effective drugs against SARS-CoV-2. In this review, we focus on the enzyme targets of the virus and host that may be critical for the discovery of chemical compounds and natural products as antiviral drugs, and describe the development of potential antiviral drugs in the preclinical and clinical stages. At the same time, we summarize novel emerging technologies applied to the research on new drug development and the pathological mechanisms of COVID-19.
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13
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Li F, Hu Q, Zhang X, Sun R, Liu Z, Wu S, Tian S, Ma X, Dai Z, Yang X, Gao S, Bai F. DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs. Nat Commun 2022; 13:7133. [PMID: 36414666 PMCID: PMC9681730 DOI: 10.1038/s41467-022-34807-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 11/08/2022] [Indexed: 11/24/2022] Open
Abstract
The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a proposed PROTAC molecule based on structures of given target protein and E3 ligase. The experimental dataset is mainly collected from PROTAC-DB and appropriately labeled according to the DC50 and Dmax values. In the model of DeepPROTACs, the ligands as well as the ligand binding pockets are generated and represented with graphs and fed into Graph Convolutional Networks for feature extraction. While SMILES representations of linkers are fed into a Bidirectional Long Short-Term Memory layer to generate the features. Experiments show that DeepPROTACs model achieves 77.95% average prediction accuracy and 0.8470 area under receiver operating characteristic curve on the test set. DeepPROTACs is available online at a web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepprotacs/ ) and at github ( https://github.com/fenglei104/DeepPROTACs ).
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Affiliation(s)
- Fenglei Li
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Qiaoyu Hu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Xianglei Zhang
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Renhong Sun
- Gluetacs Therapeutics (Shanghai) Co., Ltd., 99 Haike Road, Zhangjiang Hi-Tech Park, Shanghai, 201210, China
| | - Zhuanghua Liu
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Sanan Wu
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Siyuan Tian
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Xinyue Ma
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Zhizhuo Dai
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China
| | - Xiaobao Yang
- Gluetacs Therapeutics (Shanghai) Co., Ltd., 99 Haike Road, Zhangjiang Hi-Tech Park, Shanghai, 201210, China.
| | - Shenghua Gao
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
- School of Information Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
- School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, 201210, China.
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China.
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14
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Yerrabelly JR, Porala S, Kasireddy VR, Sony EJ, Sagurthi SR. Design, synthesis, and activity of 2-aminochromone core N,N-bis-1,2,3-triazole derivatives using click chemistry. CHEMICAL PAPERS 2022; 76:7833-7846. [PMID: 36093309 PMCID: PMC9441325 DOI: 10.1007/s11696-022-02449-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 08/22/2022] [Indexed: 11/03/2022]
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15
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Bitam S, Hamadache M, Hanini S. 2D-QSAR, docking, molecular dynamics, studies of PF-07321332 analogues to identify alternative inhibitors against 3CL pro enzyme in SARS-CoV disease. J Biomol Struct Dyn 2022:1-10. [PMID: 35983623 DOI: 10.1080/07391102.2022.2113822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
Given the results of the Pfizer-developed inhibitor PF-07321332 in the treatment of the SARS-Covid-19 epidemic, we aimed to identify potential alternatives to this compound by utilizing various methods; we developed 2 D-QSAR models to predict the therapeutic activity of 78 analogues of PF-07321332, three statistical learning techniques including (MLP-ANN), (SVR), and (MLR) were exploited. Various validation approaches were applied to the three models developed following the use of five most relevant descriptors. The study of the characteristics of these descriptors proved that the inhibitory activity of PF-07321332 analogues is specifically affected by the structure of the molecule, its polarizability, and by the hydrogen bonds. The best model, named MLP-ANN (with a 5-3-1 architecture), was selected on the basis of the following statistical parameters: r2 = 0.922, Q2 = 0.921. In addition, we performed a molecular docking and a molecular dynamics analysis of these compounds. The obtained results confirm that compound 8 can be a good alternative for compound PF-07321332.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Said Bitam
- Faculté de Technologie, Département du Génie des Procédés et Environnement, Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Médéa, Algérie
| | - Mabrouk Hamadache
- Faculté de Technologie, Département du Génie des Procédés et Environnement, Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Médéa, Algérie
| | - Salah Hanini
- Faculté de Technologie, Département du Génie des Procédés et Environnement, Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Médéa, Algérie
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16
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Gunasinghe J, Hwang SS, Yam WK, Rahman T, Wezen XC. In-silico discovery of inhibitors against human papillomavirus E1 protein. J Biomol Struct Dyn 2022:1-14. [PMID: 35751129 DOI: 10.1080/07391102.2022.2091659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
High-risk (HR) Human papillomavirus (e.g. HPV16 and HPV18) causes approximately two-thirds of all cervical cancers in women. Although the first and second-generation vaccines confer some protection against individuals, there are no approved drugs to treat HR-HPV infections to-date. The HPV E1 protein is an attractive drug target because the protein is highly conserved across all HPV types and is crucial for the regulation of viral DNA replication. Hence, we used the Random Forest algorithm to construct a Quantitative-Structure Activity Relationship (QSAR) model to predict the potential inhibitors against the HPV E1 protein. Our QSAR classification model achieved an accuracy of 87.5%, area under the receiver operating characteristic curve of 1.00, and F-measure of 0.87 when evaluated using an external test set. We conducted a drug repurposing campaign by deploying the model to screen the Drugbank database. The top three compounds, namely Cinalukast, Lobeglitazone, and Efatutazone were analyzed for their cell membrane permeability, toxicity, and carcinogenicity. Finally, these three compounds were subjected to molecular docking and 200 ns-long Molecular Dynamics (MD) simulations. The predicted binding free energies for the candidates were calculated using the MM-GBSA method. The binding free energies for Cinalukast, Lobeglitazone, and Efatutazone were -37.84 kcal/mol, -25.30 kcal/mol, and -29.89 kcal/mol respectively. Therefore, we propose their chemical scaffolds for future rational design of E1 inhibitors.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Juliyan Gunasinghe
- School of Engineering and Science, Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Malaysia
| | - Siaw San Hwang
- School of Engineering and Science, Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Malaysia
| | - Wai Keat Yam
- School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| | - Taufiq Rahman
- Department of Pharmacology, University of Cambridge, Cambridge, UK
| | - Xavier Chee Wezen
- School of Engineering and Science, Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak, Malaysia.,Department of Pharmacology, University of Cambridge, Cambridge, UK
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17
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Khazaneha M, Tajedini O, Esmaeili O, Abdi M, Khasseh AA, Sadatmoosavi A. Thematic evolution of coronavirus disease: a longitudinal co-word analysis. LIBRARY HI TECH 2022. [DOI: 10.1108/lht-10-2021-0370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PurposeUsing science mapping analysis approach and co-word analysis, the present study explores and visualizes research fields and thematic evolution of the coronavirus. Based on this method, one can get a picture of the real content of the themes in the mentioned thematic area and identify the main minor and emerging themes.Design/methodology/approachThis study was conducted based on co-word science mapping analysis under a longitudinal study (from 1988 to 2020). The collection of documents in this study was further divided into three subperiods: 1988–1998, 1999–2009 and 2010–2020. In order to perform science mapping analysis based on co-word bibliographic networks, SciMAT was utilized as a bibliometric tool. Moreover, WoS, PubMed and Scopus bibliographic databases were used to download all records.FindingsIn this study, strategic diagrams were demonstrated for the coronavirus research for a chronological period to assess the most relevant themes. Each diagram depended on the sum of documents linked to each research topic. In the first period (1988–1998), the most centralizations were on virology and evaluation of coronavirus structure and its structural and nonstructural proteins. In the second period (1999–2009), with due attention to high population density in eastern Asia and the increasing number of people affected with the new generation of coronavirus (named severe acute respiratory syndrome virus or SARS virus), publications have been concentrated on “antiviral activity.” In the third period (2010–2020), there was a tendency to investigate clinical syndromes, and most of the publications and citations were about hot topics like “severe acute respiratory syndrome,” “coronavirus” and “respiratory tract disease.” Scientometric analysis of the field of coronavirus can be regarded as a roadmap for future research and policymaking in this important area.Originality/valueThe originality of this research can be considered in two ways. First, the strategic diagrams of coronavirus are drawn in four thematic areas including motor cluster, basic and transversal cluster, highly developed cluster and emerging and declining cluster. Second, COVID-19 is mentioned as a hot topic of research.
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18
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Abdeldayem OM, Dabbish AM, Habashy MM, Mostafa MK, Elhefnawy M, Amin L, Al-Sakkari EG, Ragab A, Rene ER. Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 803:149834. [PMID: 34525746 PMCID: PMC8379898 DOI: 10.1016/j.scitotenv.2021.149834] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/05/2021] [Accepted: 08/18/2021] [Indexed: 05/06/2023]
Abstract
A viral outbreak is a global challenge that affects public health and safety. The coronavirus disease 2019 (COVID-19) has been spreading globally, affecting millions of people worldwide, and led to significant loss of lives and deterioration of the global economy. The current adverse effects caused by the COVID-19 pandemic demands finding new detection methods for future viral outbreaks. The environment's transmission pathways include and are not limited to air, surface water, and wastewater environments. The wastewater surveillance, known as wastewater-based epidemiology (WBE), can potentially monitor viral outbreaks and provide a complementary clinical testing method. Another investigated outbreak surveillance technique that has not been yet implemented in a sufficient number of studies is the surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in the air. Artificial intelligence (AI) and its related machine learning (ML) and deep learning (DL) technologies are currently emerging techniques for detecting viral outbreaks using global data. To date, there are no reports that illustrate the potential of using WBE with AI to detect viral outbreaks. This study investigates the transmission pathways of SARS-CoV-2 in the environment and provides current updates on the surveillance of viral outbreaks using WBE, viral air sampling, and AI. It also proposes a novel framework based on an ensemble of ML and DL algorithms to provide a beneficial supportive tool for decision-makers. The framework exploits available data from reliable sources to discover meaningful insights and knowledge that allows researchers and practitioners to build efficient methods and protocols that accurately monitor and detect viral outbreaks. The proposed framework could provide early detection of viruses, forecast risk maps and vulnerable areas, and estimate the number of infected citizens.
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Affiliation(s)
- Omar M Abdeldayem
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands.
| | - Areeg M Dabbish
- Biotechnology Graduate Program, Biology Department, School of Science and Engineering, The American University in Cairo, New Cairo 11835, Egypt
| | - Mahmoud M Habashy
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands
| | - Mohamed K Mostafa
- Faculty of Engineering and Technology, Badr University in Cairo (BUC), Cairo 11829, Egypt
| | - Mohamed Elhefnawy
- CanmetENERGY, 1615 Lionel-Boulet Blvd, P.O. Box 4800, Varennes, Québec J3X 1P7, Canada; Department of Mathematics and Industrial Engineering, Polytechnique Montréal 2500 Chemin de Polytechnique, Montréal, Québec H3T 1J4, Canada
| | - Lobna Amin
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands; Department of Built Environment, Aalto University, PO Box 15200, FI-00076, Aalto, Finland
| | - Eslam G Al-Sakkari
- Chemical Engineering Department, Cairo University, Cairo University Road, 12613 Giza, Egypt
| | - Ahmed Ragab
- CanmetENERGY, 1615 Lionel-Boulet Blvd, P.O. Box 4800, Varennes, Québec J3X 1P7, Canada; Department of Mathematics and Industrial Engineering, Polytechnique Montréal 2500 Chemin de Polytechnique, Montréal, Québec H3T 1J4, Canada; Faculty of Electronic Engineering, Menoufia University, 32952, Menouf, Egypt
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, 2611AX Delft, the Netherlands
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19
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Quantitative structure–activity relationship-based computational approaches. COMPUTATIONAL APPROACHES FOR NOVEL THERAPEUTIC AND DIAGNOSTIC DESIGNING TO MITIGATE SARS-COV-2 INFECTION 2022. [PMCID: PMC9300454 DOI: 10.1016/b978-0-323-91172-6.00001-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
World Health Organization (WHO) categorized novel Coronavirus disease (COVID-19), triggered by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) as a world pandemic. This infection has been increasing alarmingly by instigating enormous social and economic disturbance. In order to retort rapidly, the inhibitors previously designed against different targets will be a good starting point for anti-SARS-CoV-2 inhibitors. The chapter deals with various quantitative structure–activity relationship (QSAR) techniques currently used in computational drug design and their applications and advantages in the overall drug design process. The chapter reviews current QSAR studies carried out against SARS-COV-2. The QSAR study design is composed of some major facets: (1) classification QSAR-based data mining of various inhibitors, (2) QSAR-based virtual screening to recognize molecules that could be effective against assumed COVID-19 protein targets. (3) Finally validation of hits through receptor–ligand interaction analysis. This approach is used overall to help in the process of COVID-19 drug discovery. It presents key conceptions, sets the stage for QSAR-based screening of active molecules against SARS-COV-2. Moreover, the QSAR models reported can be further used to monitor huge databases. This chapter gives a first-hand review of all the current QSAR parameters developed for generating a good QSAR model against SARS-COV-2 and subsequently designing a drug against the COVID-19 virus.
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20
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Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2-ACE2 interface. PLoS One 2021; 16:e0256834. [PMID: 34499662 PMCID: PMC8428716 DOI: 10.1371/journal.pone.0256834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 08/16/2021] [Indexed: 11/19/2022] Open
Abstract
The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.
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21
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Glaab E, Manoharan GB, Abankwa D. Pharmacophore Model for SARS-CoV-2 3CLpro Small-Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors. J Chem Inf Model 2021; 61:4082-4096. [PMID: 34348021 PMCID: PMC8353990 DOI: 10.1021/acs.jcim.1c00258] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Indexed: 01/18/2023]
Abstract
Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of the identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small-molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.
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Affiliation(s)
- Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB),
University of Luxembourg, 7 Avenue des Hauts Fourneaux,
L-4362 Esch-sur-Alzette, Luxembourg
| | - Ganesh Babu Manoharan
- Department of Life Sciences and Medicine,
University of Luxembourg, 7 Avenue des Hauts Fourneaux,
L-4362 Esch-sur-Alzette, Luxembourg
| | - Daniel Abankwa
- Department of Life Sciences and Medicine,
University of Luxembourg, 7 Avenue des Hauts Fourneaux,
L-4362 Esch-sur-Alzette, Luxembourg
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22
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El-Rashidy N, Abdelrazik S, Abuhmed T, Amer E, Ali F, Hu JW, El-Sappagh S. Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic. Diagnostics (Basel) 2021; 11:1155. [PMID: 34202587 PMCID: PMC8303306 DOI: 10.3390/diagnostics11071155] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/29/2021] [Accepted: 05/31/2021] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.
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Affiliation(s)
- Nora El-Rashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh 13518, Egypt
| | - Samir Abdelrazik
- Information System Department, Faculty of Computer Science and Information Systems, Mansoura University, Mansoura 13518, Egypt;
| | - Tamer Abuhmed
- College of Computing and Informatics, Sungkyunkwan University, Seoul 03063, Korea
| | - Eslam Amer
- Faculty of Computer Science, Misr International University, Cairo 11828, Egypt;
| | - Farman Ali
- Department of Software, Sejong University, Seoul 05006, Korea;
| | - Jong-Wan Hu
- Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Korea
| | - Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
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23
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Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov 2021; 16:961-975. [PMID: 33957833 DOI: 10.1080/17460441.2021.1918098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Jadson Castro Gertrudes
- Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia Maria Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
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24
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Sorouri F, Emamgholipour Z, Keykhaee M, Najafi A, Firoozpour L, Sabzevari O, Sharifzadeh M, Foroumadi A, Khoobi M. The situation of small molecules targeting key proteins to combat SARS-CoV-2: Synthesis, metabolic pathway, mechanism of action, and potential therapeutic applications. Mini Rev Med Chem 2021; 22:273-311. [PMID: 33687881 DOI: 10.2174/1389557521666210308144302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 12/14/2020] [Accepted: 12/28/2020] [Indexed: 12/15/2022]
Abstract
Due to the global epidemic and high mortality of 2019 coronavirus disease (COVID-19), there is an immediate need to discover drugs that can help before a vaccine becomes available. Given that the process of producing new drugs is so long, the strategy of repurposing existing drugs is one of the promising options for the urgent treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19 disease. Although FDA has approved Remdesivir for the use in hospitalized adults and pediatric patients suffering from COVID-19, no fully effective and reliable drug has been yet identified worldwide to treat COVID-19 specifically. Thus, scientists are still trying to find antivirals specific to COVID-19. This work reviews the chemical structure, metabolic pathway, mechanism of action of existing drugs with potential therapeutic applications for COVID-19. Further, we summarized the molecular docking stimulation of the medications related to key protein targets. These already drugs could be developed for further clinical trials to supply suitable therapeutic options for patients suffering from COVID-19.
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Affiliation(s)
- Farzaneh Sorouri
- Department of Pharmaceutical Biomaterials, Faculty of Pharmacy, Tehran University of Medical Science, Tehran. Iran
| | - Zahra Emamgholipour
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Science, Tehran. Iran
| | - Maryam Keykhaee
- Department of Pharmaceutical Biomaterials, Faculty of Pharmacy, Tehran University of Medical Science, Tehran. Iran
| | - Alireza Najafi
- Department of Immunology, Faculty of Medicine, Iran University of Medical Sciences, Tehran. Iran
| | - Loghman Firoozpour
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Science, Tehran. Iran
| | - Omid Sabzevari
- Department of Toxicology and Pharmacology, Faculty of Pharmacy, Toxicology and Poisoning Research Centre, Tehran University of Medical Sciences, Tehran. Iran
| | - Mohammad Sharifzadeh
- Department of Toxicology and Pharmacology, Faculty of Pharmacy, Toxicology and Poisoning Research Centre, Tehran University of Medical Sciences, Tehran. Iran
| | - Alireza Foroumadi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Science, Tehran. Iran
| | - Mehdi Khoobi
- Department of Medicinal Chemistry, Faculty of Pharmacy, Tehran University of Medical Science, Tehran. Iran
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25
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Cooper K, Baddeley C, French B, Gibson K, Golden J, Lee T, Pierre S, Weiss B, Yang J. Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning. ACS OMEGA 2021; 6:4857-4877. [PMID: 33644594 PMCID: PMC7905939 DOI: 10.1021/acsomega.0c05303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 01/25/2021] [Indexed: 05/04/2023]
Abstract
A unique approach to bioactivity and chemical data curation coupled with random forest analyses has led to a series of target-specific and cross-validated predictive feature fingerprints (PFF) that have high predictability across multiple therapeutic targets and disease stages involved in the severe acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2)-induced COVID-19 pandemic, which include plasma kallikrein, human immunodeficiency virus (HIV)-protease, nonstructural protein (NSP)5, NSP12, Janus kinase (JAK) family, and AT-1. The approach was highly accurate in determining the matched target for the different compound sets and suggests that the models could be used for virtual screening of target-specific compound libraries. The curation-modeling process was successfully applied to a SARS-CoV-2 phenotypic screen and could be used for predictive bioactivity estimation and prioritization for clinical trial selection; virtual screening of drug libraries for the repurposing of drug molecules; and analysis and direction of proprietary data sets.
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Affiliation(s)
- Kelvin Cooper
- KC
Pharma Consulting, 1513
Harbor Drive, Sarasota, Florida 34239, United States
| | - Christopher Baddeley
- CAS,
A Division of the American Chemical Society, 2540 Olentangy River Road, Columbus, Ohio 43210-3012, United States
| | - Bernie French
- Tasseogen
Inc., 300 Mainsail Drive, Westerville, Ohio 43018, United States
| | - Katherine Gibson
- CAS,
A Division of the American Chemical Society, 2540 Olentangy River Road, Columbus, Ohio 43210-3012, United States
| | - James Golden
- WorldQuant
Predictive, 575 Fifth
Avenue, New York, New York 10017, United States
| | - Thiam Lee
- WorldQuant
Predictive, 575 Fifth
Avenue, New York, New York 10017, United States
| | - Sadrach Pierre
- WorldQuant
Predictive, 575 Fifth
Avenue, New York, New York 10017, United States
| | - Brent Weiss
- CAS,
A Division of the American Chemical Society, 2540 Olentangy River Road, Columbus, Ohio 43210-3012, United States
| | - Jason Yang
- WorldQuant
Predictive, 575 Fifth
Avenue, New York, New York 10017, United States
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26
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Rodrigues JF, Florea L, de Oliveira MCF, Diamond D, Oliveira ON. Big data and machine learning for materials science. DISCOVER MATERIALS 2021; 1:12. [PMID: 33899049 PMCID: PMC8054236 DOI: 10.1007/s43939-021-00012-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/01/2021] [Indexed: 05/11/2023]
Abstract
Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.
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Affiliation(s)
- Jose F. Rodrigues
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Larisa Florea
- SFI Research Centre for Advanced Materials and BioEngineering Research Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Maria C. F. de Oliveira
- Institute of Mathematical Sciences and Computing, University of São Paulo (USP), São Carlos, SP Brazil
| | - Dermot Diamond
- Insight Centre for Data Analytics, National Centre for Sensor Research, Dublin City University, Dublin 9, Dublin, Ireland
| | - Osvaldo N. Oliveira
- São Carlos Institute of Physics, University of São Paulo (USP), São Carlos, SP Brazil
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