1
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Karasev DA, Sobolev BN, Filimonov DA, Lagunin A. Prediction of viral protease inhibitors using proteochemometrics approach. Comput Biol Chem 2024; 110:108061. [PMID: 38574417 DOI: 10.1016/j.compbiolchem.2024.108061] [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: 12/28/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/06/2024]
Abstract
Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.
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Affiliation(s)
- Dmitry A Karasev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.
| | - Boris N Sobolev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow 117997, Russia
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2
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Lagunin AA, Sezganova AS, Muraviova ES, Rudik AV, Filimonov DA. BC CLC-Pred: a freely available web-application for quantitative and qualitative predictions of substance cytotoxicity in relation to human breast cancer cell lines. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:1-9. [PMID: 38112004 DOI: 10.1080/1062936x.2023.2289050] [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: 10/17/2023] [Accepted: 11/24/2023] [Indexed: 12/20/2023]
Abstract
In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC50 and IG50 values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction r2, RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC50 and IG50 values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.
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Affiliation(s)
- A A Lagunin
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - A S Sezganova
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - E S Muraviova
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - A V Rudik
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - D A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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3
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Khairullina V, Martynova Y. Quantitative Structure-Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity. Molecules 2023; 28:7715. [PMID: 38067446 PMCID: PMC10708366 DOI: 10.3390/molecules28237715] [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: 09/29/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 μmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC50 values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK.
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Affiliation(s)
- Veronika Khairullina
- Institute of Chemistry and Defence in Emergency Situations, Ufa University of Science and Technology, 50076 Ufa, Russia;
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4
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Khairullina V, Martynova Y, Safarova I, Sharipova G, Gerchikov A, Limantseva R, Savchenko R. QSPR Modeling and Experimental Determination of the Antioxidant Activity of Some Polycyclic Compounds in the Radical-Chain Oxidation Reaction of Organic Substrates. Molecules 2022; 27:molecules27196511. [PMID: 36235050 PMCID: PMC9572093 DOI: 10.3390/molecules27196511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/28/2022] [Accepted: 09/28/2022] [Indexed: 01/18/2023] Open
Abstract
The present work addresses the quantitative structure−antioxidant activity relationship in a series of 148 sulfur-containing alkylphenols, natural phenols, chromane, betulonic and betulinic acids, and 20-hydroxyecdysone using GUSAR2019 software. Statistically significant valid models were constructed to predict the parameter logk7, where k7 is the rate constant for the oxidation chain termination by the antioxidant molecule. These results can be used to search for new potentially effective antioxidants in virtual libraries and databases and adequately predict logk7 for test samples. A combination of MNA- and QNA-descriptors with three whole molecule descriptors (topological length, topological volume, and lipophilicity) was used to develop six statistically significant valid consensus QSPR models, which have a satisfactory accuracy in predicting logk7 for training and test set structures: R2TR > 0.6; Q2TR > 0.5; R2TS > 0.5. Our theoretical prediction of logk7 for antioxidants AO1 and AO2, based on consensus models agrees well with the experimental value of the measure in this paper. Thus, the descriptor calculation algorithms implemented in the GUSAR2019 software allowed us to model the kinetic parameters of the reactions underlying the liquid-phase oxidation of organic hydrocarbons.
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Affiliation(s)
- Veronika Khairullina
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
- Correspondence: ; Tel.: +7-963-906-6567
| | - Yuliya Martynova
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
| | - Irina Safarova
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
| | - Gulnaz Sharipova
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
| | | | - Regina Limantseva
- Institute of Petrochemistry and Catalysis of the Ufa Federal Research Center of the Russian Academy of Sciences, 450075 Ufa, Russia
| | - Rimma Savchenko
- Institute of Petrochemistry and Catalysis of the Ufa Federal Research Center of the Russian Academy of Sciences, 450075 Ufa, Russia
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5
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Hajighasemi H, Foroughifar N, Khajeh-Amiri A, Balali E. One-pot reactions in the synthesis of thiazolidinone derivatives by nano-Fe 3O 4–cysteine catalyst. J Sulphur Chem 2022. [DOI: 10.1080/17415993.2022.2089038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Halimeh Hajighasemi
- Department of Organic Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | | | | | - Ebrahim Balali
- Department of Organic Chemistry, Faculty of Pharmaceutical Chemistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
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6
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Cai Z, Zafferani M, Akande OM, Hargrove AE. Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure. J Med Chem 2022; 65:7262-7277. [PMID: 35522972 PMCID: PMC9150105 DOI: 10.1021/acs.jmedchem.2c00254] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.
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Affiliation(s)
- Zhengguo Cai
- Department
of Chemistry, Duke University, 124 Science Drive, Durham, North Carolina 27708, United States
| | - Martina Zafferani
- Department
of Chemistry, Duke University, 124 Science Drive, Durham, North Carolina 27708, United States
| | - Olanrewaju M. Akande
- Social
Science Research Institute, 140 Science Drive, Durham, North Carolina 27708, United States
| | - Amanda E. Hargrove
- Department
of Chemistry, Duke University, 124 Science Drive, Durham, North Carolina 27708, United States,. Phone: 919-660-1521. Fax: 919-660-1605
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7
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Ivanov SM, Lagunin AA, Filimonov DA, Poroikov VV. Relationships between the Structure and Severe Drug-Induced Liver Injury for Low, Medium, and High Doses of Drugs. Chem Res Toxicol 2022; 35:402-411. [PMID: 35172101 DOI: 10.1021/acs.chemrestox.1c00307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Assessment of structure-activity relationships (SARs) for predicting severe drug-induced liver injury (DILI) is essential since in vivo and in vitro preclinical methods cannot detect many druglike compounds disrupting liver functions. To date, plenty of SAR models for the prediction of DILI have been developed; however, none of them considered the route of drug administration and daily dose, which may introduce significant bias into prediction results. We have created a dataset of 617 drugs with parenteral and oral administration routes and consistent information on DILI severity. We have found a clear relationship between route, dose, and DILI severity. According to SAR, nearly 40% of moderate- and non-DILI-causing drugs would cause severe DILI if they were administered at high oral doses. We have proposed the following approach to predict severe DILI. New compounds recommended to be used at low oral doses (<∼10 mg daily), or parenterally, can be considered not causing severe DILI. DILI for compounds administered at medium oral doses (∼10-100 mg daily; 22.2% of drugs under consideration) can be considered unpredictable because reasonable SAR models were not obtained due to the small size and heterogeneity of the corresponding dataset. The DILI potential of the compounds recommended to be used at high oral doses (more than ∼100 mg daily) can be estimated using SAR modeling. The balanced accuracy of the approach calculated by a 10-fold cross-validation procedure is 0.803. The developed approach can be used to estimate severe DILI for druglike compounds proposed to use at low and high oral doses or parenterally at the early stages of drug development.
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Affiliation(s)
- Sergey M Ivanov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia.,Pirogov Russian National Research Medical University, Ostrovityanova Str., 1, Moscow 117997, Russia
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia.,Pirogov Russian National Research Medical University, Ostrovityanova Str., 1, Moscow 117997, Russia
| | - Dmitry A Filimonov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia
| | - Vladimir V Poroikov
- Institute of Biomedical Chemistry, Pogodinskaya Str., 10/8, Moscow 119121, Russia
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8
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Matsumoto K, Miyao T, Funatsu K. Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration. ACS OMEGA 2021; 6:11964-11973. [PMID: 34056351 PMCID: PMC8154010 DOI: 10.1021/acsomega.1c00463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/21/2021] [Indexed: 05/15/2023]
Abstract
In ligand-based drug design, quantitative structure-activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC50). Experimental IC50 data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.
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Affiliation(s)
- Katsuhisa Matsumoto
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
| | - Tomoyuki Miyao
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
- Data
Science Center, Nara Institute of Science
and Technology, 8916-5
Takayama-cho, Ikoma, Nara, 630-0192, Japan
| | - Kimito Funatsu
- Data
Science Center, Nara Institute of Science
and Technology, 8916-5
Takayama-cho, Ikoma, Nara, 630-0192, Japan
- Department
of Chemical System Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- E-mail: . Phone: +81-3-5841-7751. Fax: +81-3-5841-7771
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9
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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10
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QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds. Molecules 2021; 26:molecules26020421. [PMID: 33466934 PMCID: PMC7830365 DOI: 10.3390/molecules26020421] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 01/07/2021] [Accepted: 01/09/2021] [Indexed: 11/16/2022] Open
Abstract
Using the GUSAR 2013 program, the quantitative structure–antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk7 = 0.01–6.65 (where k7 is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon oxidation). Based on the atomic descriptors (Quantitative Neighborhood of Atoms (QNA) and Multilevel Neighborhoods of Atoms (MNA)) and molecular (topological length, topological volume and lipophilicity) descriptors, we have developed 9 statistically significant QSAR consensus models that demonstrate high accuracy in predicting the lgk7 values for the compounds of training sets and appropriately predict lgk7 for the test samples. Moderate predictive power of these models is demonstrated using metrics of two categories: (1) based on the determination coefficients R2 (R2TSi, R20, Q2(F1), Q2(F2), RmTSi2¯) and based on the concordance correlation coefficient (CCC)); or (2) based on the prediction lgk7 errors (root mean square error (RMSEP), mean absolute error (MAE) and standard deviation (S.D.)) The RBF-SCR method has been used for selecting the descriptors. Our theoretical prognosis of the lgk7 for 8-PPDA, a known antioxidant, based on the consensus models well agrees with the experimental value measure in the present work. Thus, the algorithms for calculating the descriptors implemented in the GUSAR 2013 program allow simulating kinetic parameters of the reactions underling the liquid-phase oxidation of hydrocarbons.
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11
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Martynova YZ, Khairullina VR, Nasretdinova RN, Garifullina GG, Mitsukova DS, Gerchikov AY, Mustafin AG. Determination of the chain termination rate constants of the radical chain oxidation of organic compounds on antioxidant molecules by the QSPR method. Russ Chem Bull 2020. [DOI: 10.1007/s11172-020-2948-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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Alves VM, Bobrowski T, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. QSAR Modeling of SARS-CoV M pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2. Mol Inform 2020; 40:e2000113. [PMID: 33405340 DOI: 10.1002/minf.202000113] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/28/2020] [Indexed: 12/22/2022]
Abstract
The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 μM), proglumetacin (tested twice independently, with AC50 of 8.9 μM and 12.5 μM), and sufugolix (AC50 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).
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Affiliation(s)
- Vinicius M Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Cleber C Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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13
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Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations. Russ Chem Bull 2020. [DOI: 10.1007/s11172-019-2683-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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14
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Pogodin PV, Lagunin AA, Rudik AV, Druzhilovskiy DS, Filimonov DA, Poroikov VV. AntiBac-Pred: A Web Application for Predicting Antibacterial Activity of Chemical Compounds. J Chem Inf Model 2019; 59:4513-4518. [PMID: 31661960 DOI: 10.1021/acs.jcim.9b00436] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.
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Affiliation(s)
- Pavel V Pogodin
- Department for Bioinformatics , Institute of Biomedical Chemistry (IBMC) , Moscow 119121 , Russia
| | - Alexey A Lagunin
- Department for Bioinformatics , Institute of Biomedical Chemistry (IBMC) , Moscow 119121 , Russia.,Department of Bioinformatics , Pirogov Russian National Research Medical University , Moscow 117997 , Russia
| | - Anastasia V Rudik
- Department for Bioinformatics , Institute of Biomedical Chemistry (IBMC) , Moscow 119121 , Russia
| | - Dmitry S Druzhilovskiy
- Department for Bioinformatics , Institute of Biomedical Chemistry (IBMC) , Moscow 119121 , Russia
| | - Dmitry A Filimonov
- Department for Bioinformatics , Institute of Biomedical Chemistry (IBMC) , Moscow 119121 , Russia
| | - Vladimir V Poroikov
- Department for Bioinformatics , Institute of Biomedical Chemistry (IBMC) , Moscow 119121 , Russia
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