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Synthesis, theoretical and experimental spectroscopic properties, molecular docking, ADMET, and RDG analysis of copper(II) complex of dichloro(1,10-phenanthroline)(1,2,4-triazole-3-carboxcylic acid). CHEMICAL PAPERS 2022. [DOI: 10.1007/s11696-022-02158-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Nayarisseri A, Khandelwal R, Tanwar P, Madhavi M, Sharma D, Thakur G, Speck-Planche A, Singh SK. Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery. Curr Drug Targets 2021; 22:631-655. [PMID: 33397265 DOI: 10.2174/1389450122999210104205732] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Revised: 08/21/2020] [Accepted: 09/14/2020] [Indexed: 11/22/2022]
Abstract
Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Ravina Khandelwal
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Poonam Tanwar
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Maddala Madhavi
- Department of Zoology, Nizam College, Osmania University, Hyderabad - 500001, Telangana State, India
| | - Diksha Sharma
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Garima Thakur
- In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. 8940577, San Joaquin, Santiago, Chile
| | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India
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Aghaee E, Ghodrati M, Ghasemi JB. In silico exploration of novel protease inhibitors against coronavirus 2019 (COVID-19). INFORMATICS IN MEDICINE UNLOCKED 2021; 23:100516. [PMID: 33457495 PMCID: PMC7801185 DOI: 10.1016/j.imu.2021.100516] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 12/24/2022] Open
Abstract
The spread of SARS-CoV-2 has affected human health globally. Hence, it is necessary to rapidly find the drug-candidates that can be used to treat the infection. Since the main protease (Mpro) is the key protein in the virus's life cycle, Mpro is served as one of the critical targets of antiviral treatment. We employed virtual screening tools to search for new inhibitors to accelerate the drug discovery process. The hit compounds were subsequently docked into the active site of SARS-CoV-2 main protease and ranked by their binding energy. Furthermore, in-silico ADME studies were performed to probe for adoption with the standard ranges. Finally, molecular dynamics simulations were applied to study the protein-drug complex's fluctuation over time in an aqueous medium. This study indicates that the interaction energy of the top ten retrieved compounds with COVID-19 main protease is much higher than the interaction energy of some currently in use protease drugs such as ML188, nelfinavir, lopinavir, ritonavir, and α-ketoamide. Among the discovered compounds, Pubchem44326934 showed druglike properties and was further analyzed by MD and MM/PBSA approaches. Besides, the constant binding free energy over MD trajectories suggests a probable drug possessing antiviral properties. MD simulations demonstrate that GLU166 and GLN189 are the most important residues of Mpro, which interact with inhibitors.
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Affiliation(s)
- Elham Aghaee
- Drug Design in Silico Lab, Chemistry Faculty, School of Sciences, University of Tehran, Tehran, Iran
| | - Marzieh Ghodrati
- Department of Neurology, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran
| | - Jahan B Ghasemi
- Drug Design in Silico Lab, Chemistry Faculty, School of Sciences, University of Tehran, Tehran, Iran
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Puratchikody A, Irfan N, Balasubramaniyan S. Conceptual design of hybrid PCSK9 lead inhibitors against coronary artery disease. BIOCATALYSIS AND AGRICULTURAL BIOTECHNOLOGY 2019. [DOI: 10.1016/j.bcab.2018.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Shiri F, Pirhadi S, Rahmani A. Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening. J Recept Signal Transduct Res 2017; 38:37-47. [PMID: 29254400 DOI: 10.1080/10799893.2017.1414844] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC50 values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski's rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.
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Affiliation(s)
- Fereshteh Shiri
- a Department of Chemistry , University of Zabol , Zabol , Iran
| | - Somayeh Pirhadi
- b Medicinal and Natural Products Chemistry Research Center , Shiraz University of Medical Sciences , Shiraz , Iran
| | - Azita Rahmani
- a Department of Chemistry , University of Zabol , Zabol , Iran
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Entezari Heravi Y, Sereshti H, Saboury AA, Ghasemi J, Amirmostofian M, Supuran CT. 3D QSAR studies, pharmacophore modeling, and virtual screening of diarylpyrazole-benzenesulfonamide derivatives as a template to obtain new inhibitors, using human carbonic anhydrase II as a model protein. J Enzyme Inhib Med Chem 2017; 32:688-700. [PMID: 28317396 PMCID: PMC6009914 DOI: 10.1080/14756366.2016.1241781] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2016] [Revised: 09/20/2016] [Accepted: 09/22/2016] [Indexed: 01/13/2023] Open
Abstract
A 3D-QSAR modeling was performed on a series of diarylpyrazole-benzenesulfonamide derivatives acting as inhibitors of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1). The compounds were collected from two datasets with the same scaffold, and utilized as a template for a new pharmacophore model to screen the ZINC database of commercially available derivatives. The datasets were divided into training, test, and validation sets. As the first step, comparative molecular field analysis (CoMFA), CoMFA region focusing and comparative molecular similarity indices analysis (CoMSIA) in parallel with docking studies were applied to a set of 41 human (h) CA II inhibitors. The validity and the prediction capacity of the resulting models were evaluated by leave-one-out (LOO) cross-validation approach. The reliability of the model for the prediction of possibly new CA inhibitors was also tested.
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Affiliation(s)
| | - Hassan Sereshti
- Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran
| | - Ali Akbar Saboury
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Jahan Ghasemi
- Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran
| | - Marzieh Amirmostofian
- Department of Medicinal Chemistry, School of Pharmacy, Zabol University of Medical Sciences, Zabol, Iran
| | - Claudiu T. Supuran
- Dipartimento Neurofarba, Universita degli Studi di Firenze, Sezione di Scienze Farmaceutiche, Florence, Sesto Fiorentino, Italy
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Characterization of the interaction of glycyrrhizin and glycyrrhetinic acid with bovine serum albumin by spectrophotometric-gradient flow injection titration technique and molecular modeling simulations. Int J Biol Macromol 2017; 102:92-103. [PMID: 28377238 DOI: 10.1016/j.ijbiomac.2017.02.106] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 02/28/2017] [Accepted: 02/28/2017] [Indexed: 01/19/2023]
Abstract
In this research, the interactions of glycyrrhizin (GL) and glycyrrhetinic acid (GA) with bovine serum albumin (BSA) have been investigated by the novel method of spectrophotometric- gradient flow injection titration technique. The hard-modeling multivariate approach to binding was used for calculation of binding constants and estimation of concentration-spectral profiles of equilibrium species. The stoichiometric ratio of binding was estimated using eigenvalue analysis. Results showed that GL and GA bind BSA with overall binding constants of KGL-BSA=3.85 (±0.09)×104Lmol-1, KGA-BSA=3.08 (±0.08)×104Lmol-1. Ligand-BSA complexes were further analyzed by combined docking and molecular dynamics (MD) simulations. Docking simulations were performed to obtain a first guess on the binding structure of the GL/GA-BSA complex, and subsequently analyzed by 20 ns MD simulations in order to evaluate interactions of GL/GA with BSA in detail. Results of MD simulations indicated that GL-BSA complex forms mainly on the basis of hydrogen bonds, while, GA-BSA complex forms on the basis of hydrophobic interactions. Also, water molecules can bridge between the ligand and protein by hydrogen bonds, which are stable during the entire simulation and play an important role in stabilization of the GL/GA-BSA complexes.
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In silico approaches to explore structure of new GPR 119 agonists for treatment of type 2 diabetes mellitus. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1808-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Abdolmaleki A, Ghasemi F, Ghasemi JB. Computer-aided drug design to explore cyclodextrin therapeutics and biomedical applications. Chem Biol Drug Des 2017; 89:257-268. [DOI: 10.1111/cbdd.12825] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Revised: 06/28/2016] [Accepted: 07/04/2016] [Indexed: 12/22/2022]
Affiliation(s)
- Azizeh Abdolmaleki
- Department of Chemistry; Faculty of Sciences; Toyserkan Branch; Islamic Azad University; Toyserkan Iran
| | | | - Jahan B. Ghasemi
- Drug Design in Silico Lab.; Chemistry Faculty; University of Tehran; Tehran Iran
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Manouchehri F, Izadmanesh Y, Aghaee E, Ghasemi JB. Experimental, computational and chemometrics studies of BSA-vitamin B6 interaction by UV–Vis, FT-IR, fluorescence spectroscopy, molecular dynamics simulation and hard-soft modeling methods. Bioorg Chem 2016; 68:124-36. [DOI: 10.1016/j.bioorg.2016.07.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Revised: 07/28/2016] [Accepted: 07/29/2016] [Indexed: 12/13/2022]
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Izadmanesh Y, Ghasemi JB. Thermodynamic study of β-cyclodextrin-dye inclusion complexes using gradient flow injection technique and molecular modeling. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2016; 165:54-60. [PMID: 27111153 DOI: 10.1016/j.saa.2016.04.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2015] [Revised: 03/07/2016] [Accepted: 04/13/2016] [Indexed: 06/05/2023]
Abstract
Gradient flow injection technique-diode array spectrophotometry was applied for β-cyclodextrin (β-CD)-dye inclusion complex studies. A single injection of a small amount of mixed β-CD-dye solution (100μl) into the carrier solution of the dye and recording the spectra gave the titration data. The mole ratio data were calculated by calibrating the dispersion pattern using a calibrator dye (rose bengal). Model-based multivariate methods were used to analyze the spectral-mole ratio data and, as a result, estimate stability constants and concentration-spectral profiles. Reliability was tested by applying this method to study the β-CD host-guest complexes with several dyes as guest molecules. Singular value decomposition (SVD) was used to select the chemical model and reduce noise. Molecular modeling provided the ability to predict the guest conformation-orientation (posing) within the cavity of β-CD and the nature of the involved interactions. Among those dyes showing observable spectral variation, the stoichiometric ratio of β-CD: dye (and log Kf) of methyl orange, fluorescein, phenol red, 4-(2-pyridylazo) resorcinol (PAR), and crystal violet were calculated to be 1:1 (4.26±0.01), 1:1 (1.53±0.08), 1:1 (3.11±0.04), 1:1 (1.06±0.12), and 2:1 (5.27±0.03), respectively. Compared with the classical method of titration, this method is simple and fast and has the advantage of needing reduced human interference. Molecular modeling facilitates a better understanding of the type of interactions and conformation of guest molecules in the β-CD cavity. The details of the proposed method are discussed in this paper.
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Affiliation(s)
- Y Izadmanesh
- Faculty of Chemistry, K. N. Toosi University of Technology, Tehran, Iran
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Santos LH, Ferreira RS, Caffarena ER. Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors. Mem Inst Oswaldo Cruz 2016; 110:847-64. [PMID: 26560977 PMCID: PMC4660614 DOI: 10.1590/0074-02760150239] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 09/08/2015] [Indexed: 01/05/2023] Open
Abstract
Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency
virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts
against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors
(NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the
highly active antiretroviral therapy in combination with other anti-HIV drugs.
However, the rapid emergence of drug-resistant viral strains has limited the
successful rate of the anti-HIV agents. Computational methods are a significant part
of the drug design process and indispensable to study drug resistance. In this
review, recent advances in computer-aided drug design for the rational design of new
compounds against HIV-1 RT using methods such as molecular docking, molecular
dynamics, free energy calculations, quantitative structure-activity relationships,
pharmacophore modelling and absorption, distribution, metabolism, excretion and
toxicity prediction are discussed. Successful applications of these methodologies are
also highlighted.
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Affiliation(s)
| | - Rafaela Salgado Ferreira
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brasil
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Ardakani A, Ghasemi JB. Identification of novel inhibitors of HIV-1 integrase using pharmacophore-based virtual screening combined with molecular docking strategies. Med Chem Res 2013. [DOI: 10.1007/s00044-013-0545-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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