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Current Topics in Medicinal Chemistry

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ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

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Research Article

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

Author(s): Anuraj Nayarisseri*, Ravina Khandelwal, Maddala Madhavi, Chandrabose Selvaraj, Umesh Panwar, Khushboo Sharma, Tajamul Hussain and Sanjeev Kumar Singh*

Volume 20, Issue 24, 2020

Page: [2146 - 2167] Pages: 22

DOI: 10.2174/1568026620666200704135327

Price: $65

Abstract

Background: The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

Objective: This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

Methods: 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

Results: Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

Conclusion: Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Keywords: COVID-19, COVID-19 protease inhibitors, Machine learning, Shape-based ML, Molecular docking, Molecular dynamics simulation, Remdesivir, nCorv-EMBS.

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[1]
Spaan, W.; Cavanagh, D.; Horzinek, M.C. Coronaviruses: structure and genome expression. J. Gen. Virol., 1988, 69(Pt 12), 2939-2952.
[http://dx.doi.org/10.1099/0022-1317-69-12-2939 ] [PMID: 3058868]
[2]
Zhao, Y.; Zhao, Z.; Wang, Y.; Zhou, Y.; Ma, Y.; Zuo, W. Singlecell RNA expression profiling of ACE2, the putative receptor of Wuhan 2019-nCov BioRxiv, 2020. (in press)
[3]
Cui, J.; Li, F.; Shi, Z.L. Origin and evolution of pathogenic coronaviruses. Nat. Rev. Microbiol., 2019, 17(3), 181-192.
[http://dx.doi.org/10.1038/s41579-018-0118-9 ] [PMID: 30531947]
[4]
St John, S.E.; Tomar, S.; Stauffer, S.R.; Mesecar, A.D. Targeting zoonotic viruses: Structure-based inhibition of the 3C-like protease from bat coronavirus HKU4--The likely reservoir host to the human coronavirus that causes Middle East Respiratory Syndrome (MERS). Bioorg. Med. Chem., 2015, 23(17), 6036-6048.
[http://dx.doi.org/10.1016/j.bmc.2015.06.039 ] [PMID: 26190463]
[5]
Cui, Q.; Huang, C.; Ji, X.; Zhang, W.; Zhang, F.; Wang, L. Possible Inhibitors of ACE2, the Receptor of 2019-nCoV, 2020. (in press)
[6]
Richardson, P.; Griffin, I.; Tucker, C.; Smith, D.; Oechsle, O.; Phelan, A.; Stebbing, J. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet, 2020, 395(10223), e30-e31.
[7]
Schor, S.; Einav, S. Repurposing of kinase inhibitors as broad-spectrum antiviral drugs. DNA Cell Biol., 2018, 37(2), 63-69.
[http://dx.doi.org/10.1089/dna.2017.4033 ] [PMID: 29148875]
[8]
Lai, M.M.C.; Holmes, K.V. Coronaviridae: the viruses and their replication. In: Fields virology; Knipe, D.M.; Howley, P.M., Eds.; Lippincott Williams & Wilkins: Philadelphia, 2001, pp. 1163-1179.
[9]
Anand, K.; Ziebuhr, J.; Wadhwani, P.; Mesters, J.R.; Hilgenfeld, R. Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs. Science, 2003, 300(5626), 1763-1767.
[http://dx.doi.org/10.1126/science.1085658 ] [PMID: 12746549]
[10]
Zhang, L.; Lin, D.; Sun, X.; Curth, U.; Drosten, C.; Sauerhering, L.; Becker, S.; Rox, K.; Hilgenfeld, R. Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science, 2020, 368(6489), 409-412.
[http://dx.doi.org/10.1126/science.abb3405 ] [PMID: 32198291]
[11]
Khandelwal, R.; Chauhan, A.P.S.; Bilawat, S.; Gandhe, A.; Hussain, T.; Hood, E.A.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high-affinity small molecule towards stat3 for the clinical treatment of osteosarcoma. Curr. Top. Med. Chem., 2018, 18(29), 2511-2526.
[http://dx.doi.org/10.2174/1568026618666181115092001 ] [PMID: 30430945]
[12]
Sharda, S.; Khandelwal, R.; Adhikary, R.; Sharma, D.; Majhi, M.; Hussain, T. A computer-aided drug designing for pharmacological inhibition of ALK inhibitors induces apoptosis and differentiation in Non-small cell lung cancer. Curr. Top. Med. Chem., 2019, 19, 1129-1144.
[http://dx.doi.org/10.2174/1568026619666190521084941 ] [PMID: 31109278]
[13]
Sweta, J.; Khandelwal, R.; Srinitha, S.; Pancholi, R.; Adhikary, R.; Ali, M.A.; Nayarisseri, A.; Vuree, S.; Singh, S.K. Identification of high-affinity small molecule targeting IDH2 for the clinical treatment of acute myeloid leukemia. Asian Pacific journal of cancer prevention. Asian Pac. J. Cancer Prev., 2019, 20(8), 2287-2297.
[http://dx.doi.org/10.31557/APJCP.2019.20.8.2287 ] [PMID: 31450897]
[14]
Xu, X.; Dang, Z. Promising Inhibitor for 2019-nCoV in Drug Development 2020.
[15]
Liu, X.; Zhang, B.; Jin, Z.; Yang, H.; Rao, Z. The crystal structure of COVID-19 main protease in complex with an inhibitor N3, 2020. (in press)
[16]
Al-Gheethi, A.; Noman, E.; Al-Maqtari, Q. A.; Hezam, K.; Mohamed, R.; Talip, B.; Ismail, N. Novel coronavirus (2019-ncov) outbreak; a systematic review for published papers. A Systematic Review for Published Papers , 2020.
[17]
Liu, X.; Wang, X.J. Potential inhibitors for 2019-nCoV coronavirus M protease from clinically approved medicines. bioRxiv, 2020.
[18]
Lu, H. Drug treatment options for the 2019-new coronavirus (2019-nCoV). Biosci. Trends, 2020. (in press).
[19]
Vincent, M.J.; Bergeron, E.; Benjannet, S.; Erickson, B.R.; Rollin, P.E.; Ksiazek, T.G.; Seidah, N.G.; Nichol, S.T. Chloroquine is a potent inhibitor of SARS coronavirus infection and spread. Virol. J., 2005, 2(1), 69.
[http://dx.doi.org/10.1186/1743-422X-2-69 ] [PMID: 16115318]
[20]
Wang, M.; Cao, R.; Zhang, L.; Yang, X.; Liu, J.; Xu, M.; Shi, Z.; Hu, Z.; Zhong, W.; Xiao, G. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res., 2020, 30(3), 269-271.
[http://dx.doi.org/10.1038/s41422-020-0282-0 ] [PMID: 32020029]
[21]
Zhavoronkov, A.; Aladinskiy, V.; Zhebrak, A.; Zagribelnyy, B.; Terentiev, V.; Bezrukov, D.S.; Polykovskiy, D.; Shayakhmetov, R.; Filimonov, A.; Orekhov, P.; Yan, Y. Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. Insilico Medicine Hong Kong Ltd A, 2020, 307, , E1.
[22]
Daina, A.; Zoete, V. A boiled‐egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 2016, 11(11), 1117-1121.
[http://dx.doi.org/10.1002/cmdc.201600182 ] [PMID: 27218427]
[23]
Warr, W.A. Representation of chemical structures. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1(4), 557-579.
[http://dx.doi.org/10.1002/wcms.36]
[24]
Miller, M.A. Chemical database techniques in drug discovery. Nat. Rev. Drug Discov., 2002, 1(3), 220-227.
[http://dx.doi.org/10.1038/nrd745 ] [PMID: 12120506]
[25]
Baskin, I.I. The power of deep learning to ligand-based novel drug discovery. Expert Opin. Drug Discov., 2020, 15(7), 755-764.
[http://dx.doi.org/10.1080/17460441.2020.1745183 ] [PMID: 32228116]
[26]
Kotsias, P.C.; Arús-Pous, J.; Chen, H.; Engkvist, O.; Tyrchan, C.; Bjerrum, E.J. Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nature Machine Intelligence, 2020, 2(5), 254-265.
[http://dx.doi.org/10.1038/s42256-020-0174-5]
[27]
Gebauer, N.; Gastegger, M.; Schütt, K. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. Proceedings of Advances in Neural Information Processing Systems; , 2019, pp. 7566-7578.
[28]
Gobbi, A.; Lee, M.L. Handling of tautomerism and stereochemistry in compound registration. J. Chem. Inf. Model., 2012, 52(2), 285-292.
[http://dx.doi.org/10.1021/ci200330x ] [PMID: 22148511]
[29]
Bjerrum, E.J.; Threlfall, R. Molecular generation with recurrent neural networks (RNNs) arXiv preprint, 2017. (in press)
[30]
Ranjan, A.; Bolkart, T.; Sanyal, S.; Black, M.J. Generating 3D faces using convolutional mesh autoencoders. Proceedings of the European Conference on Computer Vision (ECCV), Springer: Cham. 2018, 11214 , pp. 704-720.
[http://dx.doi.org/10.1007/978-3-030-01219-9_43]
[31]
Shu, Z.; Sahasrabudhe, M.; Alp Guler, R.; Samaras, D.; Paragios, N.; Kokkinos, I. Deforming autoencoders: Unsupervised disentangling of shape and appearance. Proceedings of the European conference on computer vision (ECCV), 2018, pp. 650-665.
[http://dx.doi.org/10.1007/978-3-030-01249-6_40]
[32]
Li, H.; Misra, S. Long short-term memory and variational autoencoder with convolutional neural networks for generating nmr t2 distributions. IEEE Geosci. Remote Sens. Lett., 2018, 16(2), 192-195.
[http://dx.doi.org/10.1109/LGRS.2018.2872356]
[33]
Hu, F.; Xia, G.S.; Hu, J.; Zhang, L. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens., 2015, 7(11), 14680-14707.
[http://dx.doi.org/10.3390/rs71114680]
[34]
Kuzminykh, D.; Polykovskiy, D.; Kadurin, A.; Zhebrak, A.; Baskov, I.; Nikolenko, S.; Shayakhmetov, R.; Zhavoronkov, A. 3d molecular representations based on the wave transform for convolutional neural networks. Mol. Pharm., 2018, 15(10), 4378-4385.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b01134 ] [PMID: 29473756]
[35]
Xia, M.; Li, T.; Xu, L.; Liu, L.; De Silva, C.W. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans. Mechatron., 2017, 23(1), 101-110.
[http://dx.doi.org/10.1109/TMECH.2017.2728371]
[36]
Branson, K.M.; Smith, B.J. The role of virtual screening in computer aided structure-based drug design. Aust. J. Chem., 2004, 57(11), 1029-1037.
[http://dx.doi.org/10.1071/CH04161]
[37]
Chen, B.; Wang, H.; Ding, Y.; Wild, D. Semantic breakthrough in drug discovery. Synthesis Lectures on the Semantic Web: Theory and Technology, 2014, 4(2), 1-142.
[http://dx.doi.org/10.2200/S00600ED1V01Y201409WEB009]
[38]
Sperandio, O.; Miteva, M.A.; Delfaud, F.; Villoutreix, B.O. Receptor-based computational screening of compound databases: the main docking-scoring engines. Curr. Protein Pept. Sci., 2006, 7(5), 369-393.
[http://dx.doi.org/10.2174/138920306778559377 ] [PMID: 17073691]
[39]
Alsenz, J. The impact of solubility and dissolution assessment on formulation strategy and implications for oral drug disposition Encyclopedia of Drug Metabolism and Interactions; , 2011, pp. 1-70.
[40]
Murgia, X.; Loretz, B.; Hartwig, O.; Hittinger, M.; Lehr, C.M. The role of mucus on drug transport and its potential to affect therapeutic outcomes. Adv. Drug Deliv. Rev., 2018, 124, 82-97.
[http://dx.doi.org/10.1016/j.addr.2017.10.009 ] [PMID: 29106910]
[41]
Ghareeb, M.; Akhlaghi, F. Alternative matrices for therapeutic drug monitoring of immunosuppressive agents using LC-MS/MS. Bioanalysis, 2015, 7(8), 1037-1058.
[http://dx.doi.org/10.4155/bio.15.35 ] [PMID: 25966013]
[42]
Pannu, H.S.; Singh, D.; Malhi, A.K. Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring. Neural Comput. Appl., 2019, 31, 2195-2205.
[http://dx.doi.org/10.1007/s00521-017-3181-7]
[43]
Yuan, J.; Liu, T.; Li, H.; Shi, T.; Xu, J.; Liu, H.; Wang, Z.; Wang, Q.; Xu, L.; Wang, Y.; Li, S. Oral sustained-release suspension based on a novel taste-masked and mucoadhesive carrier-ion-exchange fiber. Int. J. Pharm., 2014, 472(1-2), 74-81.
[http://dx.doi.org/10.1016/j.ijpharm.2014.05.048 ] [PMID: 24882038]
[44]
Hawkins, P.C.; Skillman, A.G.; Nicholls, A. Comparison of shape-matching and docking as virtual screening tools. J. Med. Chem., 2007, 50(1), 74-82.
[http://dx.doi.org/10.1021/jm0603365 ] [PMID: 17201411]
[45]
Akella, L.B.; DeCaprio, D. Cheminformatics approaches to analyze diversity in compound screening libraries. Curr. Opin. Chem. Biol., 2010, 14(3), 325-330.
[http://dx.doi.org/10.1016/j.cbpa.2010.03.017 ] [PMID: 20457001]
[46]
Hahn, M. Three-dimensional shape-based searching of conformationally flexible compounds. J. Chem. Inf. Comput. Sci., 1997, 37(1), 80-86.
[http://dx.doi.org/10.1021/ci960108r]
[47]
Sastry, G.M.; Dixon, S.L.; Sherman, W. Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring. J. Chem. Inf. Model., 2011, 51(10), 2455-2466.
[http://dx.doi.org/10.1021/ci2002704 ] [PMID: 21870862]
[48]
Zheng, W.; Tropsha, A. Novel variable selection quantitative structure--property relationship approach based on the k-nearest-neighbor principle. J. Chem. Inf. Comput. Sci., 2000, 40(1), 185-194.
[http://dx.doi.org/10.1021/ci980033m ] [PMID: 10661566]
[49]
Venkatachalam, C.M.; Jiang, X.; Oldfield, T.; Waldman, M. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graph. Model., 2003, 21(4), 289-307.
[http://dx.doi.org/10.1016/S1093-3263(02)00164-X ] [PMID: 12479928]
[50]
Ebejer, J.P.; Morris, G.M.; Deane, C.M. Freely available conformer generation methods: how good are they? J. Chem. Inf. Model., 2012, 52(5), 1146-1158.
[http://dx.doi.org/10.1021/ci2004658 ] [PMID: 22482737]
[51]
Landrum, G. Rdkit documentation. Release, 2013, 1, 1-79.
[52]
Tosco, P.; Stiefl, N.; Landrum, G. Bringing the MMFF force field to the RDKit: implementation and validation. J. Cheminform., 2014, 6(1), 37.
[http://dx.doi.org/10.1186/s13321-014-0037-3]
[53]
Martinho, N.; Silva, L.C.; Florindo, H.F.; Brocchini, S.; Barata, T.; Zloh, M. Practical computational toolkits for dendrimers and dendrons structure design. J. Comput. Aided Mol. Des., 2017, 31(9), 817-827.
[http://dx.doi.org/10.1007/s10822-017-0041-6 ] [PMID: 28916961]
[54]
Dubbeldam, D.; Vreede, J.; Vlugt, T.J.; Calero, S. Highlights of (bio-) chemical tools and visualization software for computational science. Curr. Opin. Chem. Eng., 2019, 23, 1-13.
[http://dx.doi.org/10.1016/j.coche.2019.02.001]
[55]
González-Medina, M.; Naveja, J.J.; Sánchez-Cruz, N.; Medina-Franco, J.L. Open chemoinformatic resources to explore the structure, properties and chemical space of molecules. RSC Advances, 2017, 7(85), 54153-54163.
[http://dx.doi.org/10.1039/C7RA11831G]
[56]
Avgy-David, H.H.; Senderowitz, H. Toward focusing conformational ensembles on bioactive conformations: a molecular mechanics/quantum mechanics study. J. Chem. Inf. Model., 2015, 55(10), 2154-2167.
[http://dx.doi.org/10.1021/acs.jcim.5b00259 ] [PMID: 26406154]
[57]
Bleiziffer, P.; Schaller, K.; Riniker, S. Machine learning of partial charges derived from high-quality quantum-mechanical calculations. J. Chem. Inf. Model., 2018, 58(3), 579-590.
[http://dx.doi.org/10.1021/acs.jcim.7b00663 ] [PMID: 29461814]
[58]
Yao, K.; Herr, J.E.; Toth, D.W.; Mckintyre, R.; Parkhill, J. The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Chem. Sci. (Camb.), 2018, 9(8), 2261-2269.
[http://dx.doi.org/10.1039/C7SC04934J ] [PMID: 29719699]
[59]
Lipinski, C.; Maltarollo, V.; Oliveira, P.; da Silva, A.; Honorio, K. Advances and perspectives in applying deep learning for drug design and discovery. Front. Robot. AI, 2019, 6, 108.
[http://dx.doi.org/10.3389/frobt.2019.00108]
[60]
Heller, S.R.; McNaught, A.; Pletnev, I.; Stein, S.; Tchekhovskoi, D. InChI, the IUPAC international chemical identifier. J. Cheminform., 2015, 7(1), 23.
[http://dx.doi.org/10.1186/s13321-015-0068-4 ] [PMID: 26136848]
[61]
Ståhl, N.; Falkman, G.; Karlsson, A.; Mathiason, G.; Boström, J. Deep reinforcement learning for multiparameter optimization in de novo drug design. J. Chem. Inf. Model., 2019, 59(7), 3166-3176.
[http://dx.doi.org/10.1021/acs.jcim.9b00325 ] [PMID: 31273995]
[62]
Uzunova, H.; Schultz, S.; Handels, H.; Ehrhardt, J. Unsupervised pathology detection in medical images using conditional variational autoencoders. Int. J. CARS, 2019, 14(3), 451-461.
[http://dx.doi.org/10.1007/s11548-018-1898-0 ] [PMID: 30542975]
[63]
Pu, Y.; Gan, Z.; Henao, R.; Yuan, X.; Li, C.; Stevens, A.; Carin, L. Variational autoencoder for deep learning of images, labels and captions. Advances in neural information processing systems, 2006, 2352-2360.
[64]
Dilokthanakul, N.; Mediano, P.A.; Garnelo, M.; Lee, M.C.; Salimbeni, H.; Arulkumaran, K.; Shanahan, M. Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv preprint , 2016. (in press)
[65]
McQueen, J.; Meilă, M.; VanderPlas, J.; Zhang, Z. Megaman: scalable manifold learning in python. J. Mach. Learn. Res., 2016, 17(1), 5176-5180.
[66]
Frydenberg, M.; Xu, J. Easy as py: A first course in python with a taste of data analytics. Inf. Syst. Educ. J., 2019, 17(4), 4.
[67]
Grüning, B.; Dale, R.; Sjödin, A.; Chapman, B.A.; Rowe, J.; Tomkins-Tinch, C.H.; Valieris, R.; Köster, J. Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat. Methods, 2018, 15(7), 475-476.
[http://dx.doi.org/10.1038/s41592-018-0046-7 ] [PMID: 29967506]
[68]
Lopez-Martin, M.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot. Sensors (Basel), 2017, 17(9), 1967.
[http://dx.doi.org/10.3390/s17091967 ] [PMID: 28846608]
[69]
Lim, J.; Ryu, S.; Kim, J.W.; Kim, W.Y. Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminform., 2018, 10(1), 31.
[http://dx.doi.org/10.1186/s13321-018-0286-7 ] [PMID: 29995272]
[70]
Pagnoni, A.; Liu, K.; Li, S. Conditional variational autoencoder for neural machine translation. arXiv preprint, 2018. (in press)
[71]
Wang, T.; Wan, X. T-CVAE: Transformer-based conditioned variational autoencoder for story completion. IJCAI , 2019, 5233-5239.
[72]
Zhao, T.; Zhao, R.; Eskenazi, M. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. arXiv preprint , 2017. (in press)
[73]
Zhang, Y.; Wang, Y.; Zhang, L.; Zhang, Z.; Gai, K. Improve diverse text generation by self labeling conditional variational auto encoder. ICASSP, 2019-2019IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Brighton, United Kingdom,. 2019, pp. 2767-2771.
[http://dx.doi.org/10.1109/ICASSP.2019.8683090]
[74]
Neylon, J.; Qi, X.; Sheng, K.; Staton, R.; Pukala, J.; Manon, R.; Low, D.A.; Kupelian, P.; Santhanam, A. A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image registration. Med. Phys., 2015, 42(1), 232-243.
[http://dx.doi.org/10.1118/1.4903504 ] [PMID: 25563263]
[75]
Hemmat, H.J.; Bondarev, E.; Dubbelman, G.; de With, P.H. Improved ICP-based pose estimation by distance-aware 3D mapping. 2014 International Conference on Computer Vision Theory and Applications, 2014, 3, pp. 360-367.
[76]
Eid, A.H.; Rashad, S.S.; Farag, A.A. A general-purpose platform for 3-D reconstruction from sequence of images. Proceedings of the Fifth International Conference on Information Fusion. FUSION, Annapolis, MD, USA. 2002, 1, pp. 425-431.
[77]
Hsu, C.C.; Hwang, H.T.; Wu, Y.C.; Tsao, Y.; Wang, H.M. Voice conversion from non-parallel corpora using variational auto-encoder. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA),, Jeju . 2016, pp. 1-6.
[http://dx.doi.org/10.1109/APSIPA.2016.7820786]
[78]
Xu, H.; Chen, W.; Zhao, N.; Li, Z.; Bu, J.; Li, Z. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. Proceedings of the 2018 World Wide Web Conference, 2018, pp. 187-196.
[http://dx.doi.org/10.1145/3178876.3185996]
[79]
Han, K.; Wen, H.; Shi, J.; Lu, K.H.; Zhang, Y.; Fu, D.; Liu, Z. Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex. Neuroimage, 2019, 198, 125-136.
[http://dx.doi.org/10.1016/j.neuroimage.2019.05.039 ] [PMID: 31103784]
[80]
Li, Y.; Pan, Q.; Wang, S.; Peng, H.; Yang, T.; Cambria, E. Disentangled variational auto-encoder for semi-supervised learning. Inf. Sci., 2019, 482, 73-85.
[http://dx.doi.org/10.1016/j.ins.2018.12.057]
[81]
Jang, M.; Seo, S.; Kang, P. Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning. Inf. Sci., 2019, 490, 59-73.
[http://dx.doi.org/10.1016/j.ins.2019.03.066]
[82]
Pu, Y.; Gan, Z.; Henao, R.; Yuan, X.; Li, C.; Stevens, A.; Carin, L. Variational autoencoder for deep learning of images, labels and captions. Advances in neural information processing systems, 2016. (in press)
[83]
Park, D.; Hoshi, Y.; Kemp, C.C. A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robot. Autom. Lett., 2018, 3(3), 1544-1551.
[http://dx.doi.org/10.1109/LRA.2018.2801475]
[84]
Sattarov, B.; Baskin, I.I.; Horvath, D.; Marcou, G.; Bjerrum, E.J.; Varnek, A. De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping. J. Chem. Inf. Model., 2019, 59(3), 1182-1196.
[http://dx.doi.org/10.1021/acs.jcim.8b00751 ] [PMID: 30785751]
[85]
Skalic, M.; Jiménez, J.; Sabbadin, D.; De Fabritiis, G. Shape-based generative modeling for de novo drug design. J. Chem. Inf. Model., 2019, 59(3), 1205-1214.
[http://dx.doi.org/10.1021/acs.jcim.8b00706 ] [PMID: 30762364]
[86]
Goh, G.B.; Hodas, N.O.; Vishnu, A. Deep learning for computational chemistry. J. Comput. Chem., 2017, 38(16), 1291-1307.
[http://dx.doi.org/10.1002/jcc.24764 ] [PMID: 28272810]
[87]
Polykovskiy, D.; Zhebrak, A.; Vetrov, D.; Ivanenkov, Y.; Aladinskiy, V.; Mamoshina, P.; Bozdaganyan, M.; Aliper, A.; Zhavoronkov, A.; Kadurin, A. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol. Pharm., 2018, 15(10), 4398-4405.
[http://dx.doi.org/10.1021/acs.molpharmaceut.8b00839 ] [PMID: 30180591]
[88]
Li, J.; Xu, K.; Chaudhuri, S.; Yumer, E.; Zhang, H.; Guibas, L. Grass: Generative recursive autoencoders for shape structures. ACM Trans. Graph., 2017, 36(4), 1-14.
[http://dx.doi.org/10.1145/3072959.3073637]
[89]
Laloy, E.; Hérault, R.; Lee, J.; Jacques, D.; Linde, N. Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network. Adv. Water Resour., 2017, 110, 387-405.
[http://dx.doi.org/10.1016/j.advwatres.2017.09.029]
[90]
Su, Y.; Li, J.; Plaza, A.; Marinoni, A.; Gamba, P.; Chakravortty, S. DAEN: Deep autoencoder networks for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens., 2019, 57(7), 4309-4321.
[http://dx.doi.org/10.1109/TGRS.2018.2890633]
[91]
Lu, W.T.; Su, L. transferring the style of homophonic music using recurrent neural networks and autoregressive model. ISMIR, 2018, pp. 740-746.
[92]
Chandra, B.; Sharma, R.K. Fast learning in deep neural networks. Neurocomputing, 2016, 171, 1205-1215.
[http://dx.doi.org/10.1016/j.neucom.2015.07.093]
[93]
Lu, S.Y.; Jiang, Y.J.; Lv, J.; Wu, T.X.; Yu, Q.S.; Zhu, W.L. Molecular docking and molecular dynamics simulation studies of GPR40 receptor-agonist interactions. J. Mol. Graph. Model., 2010, 28(8), 766-774.
[http://dx.doi.org/10.1016/j.jmgm.2010.02.001 ] [PMID: 20227312]
[94]
Kaya, S.; Tüzün, B.; Kaya, C.; Obot, I.B. Determination of corrosion inhibition effects of amino acids: quantum chemical and molecular dynamic simulation study. J. Taiwan. Inst. Chem. E., 2016, 58, 528-535.
[http://dx.doi.org/10.1016/j.jtice.2015.06.009]
[95]
Gao, S.; Liao, Q.; Liu, W.; Liu, Z. Effects of solid fraction on droplet wetting and vapor condensation: a molecular dynamic simulation study. Langmuir, 2017, 33(43), 12379-12388.
[http://dx.doi.org/10.1021/acs.langmuir.7b03193 ] [PMID: 28980811]
[96]
Tönsing, T.; Oldiges, C. Molecular dynamic simulation study on structure of water in crosslinked poly (N-isopropylacrylamide) hydrogels. Phys. Chem. Chem. Phys., 2001, 3(24), 5542-5549.
[http://dx.doi.org/10.1039/b109281m]
[97]
Hassan, M.; Abbas, Q.; Ashraf, Z.; Moustafa, A.A.; Seo, S.Y. Pharmacoinformatics exploration of polyphenol oxidases leading to novel inhibitors by virtual screening and molecular dynamic simulation study. Comput. Biol. Chem., 2017, 68, 131-142.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.02.012 ] [PMID: 28340400]
[98]
Cheng, H.; Zhang, S.; Liu, Q.; Li, X.; Frost, R.L. The molecular structure of kaolinite–potassium acetate intercalation complexes: A combined experimental and molecular dynamic simulation study. Appl. Clay Sci., 2015, 116, 273-280.
[http://dx.doi.org/10.1016/j.clay.2015.04.008]
[99]
Housaindokht, M.R.; Bozorgmehr, M.R.; Monhemi, H. Structural behavior of Candida antarctica lipase B in water and supercritical carbon dioxide: A molecular dynamic simulation study. J. Supercrit. Fluids, 2012, 63, 180-186.
[http://dx.doi.org/10.1016/j.supflu.2011.12.010]
[100]
Tsai, M.K.; Kuo, J.L.; Lu, J.M. The dynamics and spectroscopic fingerprint of hydroxyl radical generation through water dimer ionization: ab initio molecular dynamic simulation study. Phys. Chem. Chem. Phys., 2012, 14(38), 13402-13408.
[http://dx.doi.org/10.1039/c2cp42331f ] [PMID: 22941401]
[101]
Nayebi, P.; Zaminpayma, E. A molecular dynamic simulation study of mechanical properties of graphene–polythiophene composite with Reax force field. Phys. Lett. A, 2016, 380(4), 628-633.
[http://dx.doi.org/10.1016/j.physleta.2015.11.026]
[102]
Shoichet, B.K.; Kuntz, I.D.; Bodian, D.L. Molecular docking using shape descriptors. J. Comput. Chem., 1992, 13(3), 380-397.
[http://dx.doi.org/10.1002/jcc.540130311]
[103]
Pagadala, N.S.; Syed, K.; Tuszynski, J. Software for molecular docking: a review. Biophys. Rev., 2017, 9(2), 91-102.
[http://dx.doi.org/10.1007/s12551-016-0247-1 ] [PMID: 28510083]
[104]
Knegtel, R.M.; Kuntz, I.D.; Oshiro, C.M. Molecular docking to ensembles of protein structures. J. Mol. Biol., 1997, 266(2), 424-440.
[http://dx.doi.org/10.1006/jmbi.1996.0776 ] [PMID: 9047373]
[105]
Gschwend, D.A.; Good, A.C.; Kuntz, I.D. Molecular docking towards drug discovery. J. Mol. Recognit., 1996, 9(2), 175-186.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199603)9:2<175:AID-JMR260>3.0.CO;2-D ] [PMID: 8877811]
[106]
Shoichet, B.K.; Leach, A.R.; Kuntz, I.D. Ligand solvation in molecular docking. Proteins, 1999, 34(1), 4-16.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19990101)34:1<4:AID-PROT2>3.0.CO;2-6 ] [PMID: 10336382]
[107]
Ewing, T.J.; Kuntz, I.D. Critical evaluation of search algorithms for automated molecular docking and database screening. J. Comput. Chem., 1997, 18(9), 1175-1189.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19970715)18:9<1175:AID-JCC6>3.0.CO;2-O]
[108]
Doman, T.N.; McGovern, S.L.; Witherbee, B.J.; Kasten, T.P.; Kurumbail, R.; Stallings, W.C.; Connolly, D.T.; Shoichet, B.K. Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. J. Med. Chem., 2002, 45(11), 2213-2221.
[http://dx.doi.org/10.1021/jm010548w ] [PMID: 12014959]
[109]
Sobolev, V.; Wade, R.C.; Vriend, G.; Edelman, M. Molecular docking using surface complementarity. Proteins, 1996, 25(1), 120-129.
[http://dx.doi.org/10.1002/(SICI)1097-0134(199605)25:1<120:AID-PROT10>3.0.CO;2-M ] [PMID: 8727324]
[110]
Limaye, A.; Sweta, J.; Madhavi, M.; Mudgal, U.; Mukherjee, S.; Sharma, S.; Hussain, T.; Nayarisseri, A.; Singh, S.K. In silico insights on gd2: a potential target for pediatric neuroblastoma. Curr. Top. Med. Chem., 2019, 19(30), 2766-2781.
[http://dx.doi.org/10.2174/1568026619666191112115333 ] [PMID: 31721713]
[111]
Nayarisseri, A. Prospects of utilizing computational techniques for the treatment of human diseases. Curr. Top. Med. Chem., 2019, 19(13), 1071-1074.
[http://dx.doi.org/10.2174/156802661913190827102426 ] [PMID: 31490742]
[112]
Bandaru, S.; Sumithnath, T.G.; Sharda, S.; Lakhotia, S.; Sharma, A.; Jain, A.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Helix-coil transition signatures b-raf v600e mutation and virtual screening for inhibitors directed against mutant b-raf. Curr. Drug Metab., 2017, 18(6), 527-534.
[http://dx.doi.org/10.2174/1389200218666170503114611 ] [PMID: 28472910]
[113]
Nasr, A.B.; Ponnala, D.; Sagurthi, S.R.; Kattamuri, R.K.; Marri, V.K.; Gudala, S.; Lakkaraju, C.; Bandaru, S.; Nayarisseri, A. Molecular Docking studies of FKBP12-mTOR inhibitors using binding predictions. Bioinformation, 2015, 11(6), 307-315.
[http://dx.doi.org/10.6026/97320630011307 ] [PMID: 26229292]
[114]
Dunna, N.R.; Kandula, V.; Girdhar, A.; Pudutha, A.; Hussain, T.; Bandaru, S.; Nayarisseri, A. High affinity pharmacological profiling of dual inhibitors targeting RET and VEGFR2 in inhibition of kinase and angiogeneis events in medullary thyroid carcinoma. Asian Pac. J. Cancer Prev., 2015, 16(16), 7089-7095.
[http://dx.doi.org/10.7314/APJCP.2015.16.16.7089 ] [PMID: 26514495]
[115]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and compound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951 ] [PMID: 26400175]
[116]
Wang, Y.; Xiao, J.; Suzek, T.O.; Zhang, J.; Wang, J.; Bryant, S.H. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res., 2009, 37(2), W623-w633.
[117]
Bolton, E.E.; Wang, Y.; Thiessen, P.A.; Bryant, S.H. PubChem: integrated platform of small molecules and biological activities. Annu. Rep. Comput. Chem., 2008, 4, 217-241.
[http://dx.doi.org/10.1016/S1574-1400(08)00012-1]
[118]
Sinha, C.; Nischal, A.; Pant, K.K.; Bandaru, S.; Nayarisseri, A.; Khattri, S. Molecular docking analysis of RN18 and VEC5 in A3G-Vif inhibition. Bioinformation, 2014, 10(10), 611-616.
[http://dx.doi.org/10.6026/97320630010611 ] [PMID: 25489169]
[119]
Bandaru, S.; Marri, V.K.; Kasera, P.; Kovuri, P.; Girdhar, A.; Mittal, D.R.; Ikram, S.; Gv, R.; Nayarisseri, A. Structure based virtual screening of ligands to identify cysteinyl leukotriene receptor 1 antagonist. Bioinformation, 2014, 10(10), 652-657.
[http://dx.doi.org/10.6026/97320630010652 ] [PMID: 25489175]
[120]
Bandaru, S.; Ponnala, D.; Lakkaraju, C.; Bhukya, C.K.; Shaheen, U.; Nayarisseri, A. Identification of high affinity non-peptidic small molecule inhibitors of MDM2-p53 interactions through structure-based virtual screening strategies. Asian Pac. J. Cancer Prev., 2015, 16(9), 3759-3765.
[http://dx.doi.org/10.7314/APJCP.2015.16.9.3759 ] [PMID: 25987034]
[121]
Akare, U.R.; Bandaru, S.; Shaheen, U.; Singh, P.K.; Tiwari, G.; Singare, P.; Nayarisseri, A.; Banerjee, T. Molecular docking approaches in identification of High affinity inhibitors of Human SMO receptor. Bioinformation, 2014, 10(12), 737-742.
[http://dx.doi.org/10.6026/97320630010737 ] [PMID: 25670876]
[122]
Bandaru, S.; Alvala, M.; Akka, J.; Sagurthi, S.R.; Nayarisseri, A.; Singh, S.K.; Mundluru, H.P. Identification of small molecule as a high affinity β2 agonist promiscuously targeting wild and mutated (Thr164Ile) β 2 adrenergic receptor in the treatment of bronchial asthma. Curr. Pharm. Des., 2016, 22(34), 5221-5233.
[http://dx.doi.org/10.2174/1381612822666160513145721 ] [PMID: 27174812]
[123]
Ali, M.A.; Vuree, S.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Identification of high-affinity small molecules targeting gamma secretase for the treatment of alzheimer’s disease. Curr. Top. Med. Chem., 2019, 19(13), 1173-1187.
[http://dx.doi.org/10.2174/1568026619666190617155326 ] [PMID: 31244427]
[124]
Release, S. 2017-1: Glide XP; Schrodinger, LLC: Ney York, NY, 2017.
[125]
Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W. Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. J. Chem. Theory Comput., 2010, 6(5), 1509-1519.
[http://dx.doi.org/10.1021/ct900587b ] [PMID: 26615687]
[126]
Nayarisseri, A.; Moghni, S.M.; Yadav, M.; Kharate, J.; Sharma, P.; Chandok, K.H.; Shah, K.P. In silico investigations on HSP90 and its inhibition for the therapeutic prevention of breast cancer. J. Pharm. Res., 2013, 7(2), 150-156.
[http://dx.doi.org/10.1016/j.jopr.2013.02.020]
[127]
Shaheen, U.; Akka, J.; Hinore, J.S.; Girdhar, A.; Bandaru, S.; Sumithnath, T.G.; Nayarisseri, A.; Munshi, A. Computer aided identification of sodium channel blockers in the clinical treatment of epilepsy using molecular docking tools. Bioinformation, 2015, 11(3), 131-137.
[http://dx.doi.org/10.6026/97320630011131 ] [PMID: 25914447]
[128]
Gudala, S.; Khan, U.; Kanungo, N.; Bandaru, S.; Hussain, T.; Parihar, M.; Nayarisseri, A.; Mundluru, H.P. Identification and pharmacological analysis of high efficacy small molecule inhibitors of EGF-EGFR interactions in clinical treatment of non-small cell lung carcinoma: A computational approach. Asian Pac. J. Cancer Prev., 2015, 16(18), 8191-8196.
[http://dx.doi.org/10.7314/APJCP.2015.16.18.8191 ] [PMID: 26745059]
[129]
Babitha, P.P.; Sahila, M.M.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Molecular docking and pharmacological investigations of rivastigmine-fluoxetine and coumarin-tacrine hybrids against acetyl choline esterase. Bioinformation, 2015, 11(8), 378-386.
[http://dx.doi.org/10.6026/97320630011378 ] [PMID: 26420918]
[130]
Natchimuthu, V.; Bandaru, S.; Nayarisseri, A.; Ravi, S. Design, synthesis and computational evaluation of a novel intermediate salt of N-cyclohexyl-N-(cyclohexylcarbamoyl)-4-(trifluoromethyl) benzamide as potential potassium channel blocker in epileptic paroxysmal seizures. Comput. Biol. Chem., 2016, 64, 64-73.
[http://dx.doi.org/10.1016/j.compbiolchem.2016.05.003 ] [PMID: 27266485]
[131]
Patidar, K.; Deshmukh, A.; Bandaru, S.; Lakkaraju, C.; Girdhar, A.; Vr, G.; Banerjee, T.; Nayarisseri, A.; Singh, S.K. Virtual screening approaches in identification of bioactive compounds akin to delphinidin as potential her2 inhibitors for the treatment of breast cancer. Asian Pac. J. Cancer Prev., 2016, 17(4), 2291-2295.
[http://dx.doi.org/10.7314/APJCP.2016.17.4.2291 ] [PMID: 27221932]
[132]
Sahila, M.M.; Babitha, P.P.; Bandaru, S.; Nayarisseri, A.; Doss, V.A. Molecular docking based screening of GABA (A) receptor inhibitors from plant derivatives. Bioinformation, 2015, 11(6), 280-289.
[http://dx.doi.org/10.6026/97320630011280 ] [PMID: 26229288]
[133]
Bandaru, S.; Tarigopula, P.; Akka, J.; Marri, V.K.; Kattamuri, R.K.; Nayarisseri, A.; Mangalarapu, M.; Vinukonda, S.; Mundluru, H.P.; Sagurthi, S.R. Association of Beta 2 adrenergic receptor (Thr164Ile) polymorphism with Salbutamol refractoriness in severe asthmatics from Indian population. Gene, 2016, 592(1), 15-22.
[http://dx.doi.org/10.1016/j.gene.2016.07.043 ] [PMID: 27450915]
[134]
Khandekar, N.; Singh, S.; Shukla, R.; Tirumalaraju, S.; Bandaru, S.; Banerjee, T.; Nayarisseri, A. Structural basis for the in vitro known acyl-depsipeptide 2 (ADEP2) inhibition to Clp 2 protease from Mycobacterium tuberculosis. Bioinformation, 2016, 12(3), 92-97.
[http://dx.doi.org/10.6026/97320630012092 ] [PMID: 28149041]
[135]
Bandaru, S.; Alvala, M.; Nayarisseri, A.; Sharda, S.; Goud, H.; Mundluru, H.P.; Singh, S.K. Molecular dynamic simulations reveal suboptimal binding of salbutamol in T164I variant of β2 adrenergic receptor. PLoS One, 2017, 12(10), e0186666.
[http://dx.doi.org/10.1371/journal.pone.0186666 ] [PMID: 29053759]
[136]
Sharda, S.; Sarmandal, P.; Cherukommu, S.; Dindhoria, K.; Yadav, M.; Bandaru, S.; Sharma, A.; Sakhi, A.; Vyas, T.; Hussain, T.; Nayarisseri, A.; Singh, S.K. A virtual screening approach for the identification of high affinity small molecules targeting bcr-abl1 inhibitors for the treatment of chronic myeloid leukemia. Curr. Top. Med. Chem., 2017, 17(26), 2989-2996.
[http://dx.doi.org/10.2174/1568026617666170821124512 ] [PMID: 28828991]
[137]
Jain, D.; Udhwani, T.; Sharma, S.; Gandhe, A.; Reddy, P.B.; Nayarisseri, A.; Singh, S.K. Design of novel JAK3 Inhibitors towards Rheumatoid Arthritis using molecular docking analysis. Bioinformation, 2019, 15(2), 68-78.
[http://dx.doi.org/10.6026/97320630015068 ] [PMID: 31435152]
[138]
Mendonça-Junior, F.J.B.; Scotti, M.T.; Nayarisseri, A.; Zondegoumba, E.N.T.; Scotti, L. Natural bioactive products with antioxidant properties useful in neurodegenerative diseases. Oxid. Med. Cell. Longev., 2019, 20197151780.
[http://dx.doi.org/10.1155/2019/7151780 ] [PMID: 31210847]
[139]
Nayarisseri, A.; Hood, E.A. Advancement in microbial cheminformatics. Curr. Top. Med. Chem., 2018, 18(29), 2459-2461.
[http://dx.doi.org/10.2174/1568026619666181120121528 ] [PMID: 30457050]
[140]
Reddy, K.K.; Singh, S.K.; Tripathi, S.K.; Selvaraj, C.; Suryanarayanan, V. Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors. J. Recept. Signal Transduct. Res., 2013, 33(4), 234-243.
[http://dx.doi.org/10.3109/10799893.2013.789912 ] [PMID: 23638723]
[141]
Reddy, K.K.; Singh, S.K.; Tripathi, S.K.; Selvaraj, C. Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies. SAR QSAR Environ. Res., 2013, 24(7), 581-595.
[http://dx.doi.org/10.1080/1062936X.2013.772919 ] [PMID: 23521430]
[142]
Tripathi, S.K.; Selvaraj, C.; Singh, S.K.; Reddy, K.K. Molecular docking, QPLD, and ADME prediction studies on HIV-1 integrase leads. Med. Chem. Res., 2012, 21(12), 4239-4251.
[http://dx.doi.org/10.1007/s00044-011-9940-6]
[143]
Reddy, K.K.; Singh, S.K. Combined ligand and structure-based approaches on HIV-1 integrase strand transfer inhibitors. Chem. Biol. Interact., 2014, 218, 71-81.
[http://dx.doi.org/10.1016/j.cbi.2014.04.011 ] [PMID: 24792351]
[144]
Panwar, U.; Singh, S.K. Identification of novel pancreatic lipase inhibitors using in silico studies. Endocr. Metab. Immune Disord. Drug Targets, 2019, 19(4), 449-457.
[http://dx.doi.org/10.2174/1871530319666181128100903 ] [PMID: 30484411]
[145]
Panwar, U.; Chandra, I.; Selvaraj, C.; Singh, S.K. Current computational approaches for the development of anti-hiv inhibitors: an overview. Curr. Pharm. Des., 2019, 25(31), 3390-3405.
[http://dx.doi.org/10.2174/1381612825666190911160244 ] [PMID: 31538884]
[146]
Panwar, U.; Singh, S.K. An overview on Zika Virus and the importance of computational drug discovery. JERP, 2018, 3(2), 43-51.
[http://dx.doi.org/10.14218/JERP.2017.00025]
[147]
Doss, C.G.; Chakraborty, C.; Chen, L.; Zhu, H.; Zhu, H. Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective. BioMed Res. Int., 2014, 2014, , 895831..
[PMID: 25054154]
[148]
Sakamoto, K.; Kayanuma, M.; Inagaki, Y.; Hashimoto, T.; Shigeta, Y. In silico structural modeling and analysis of elongation factor-1 alpha and elongation factor-like protein. ACS Omega, 2019, 4(4), 7308-7316.
[http://dx.doi.org/10.1021/acsomega.8b03547 ] [PMID: 31459830]
[149]
Sahu, S.N.; Pattanayak, S.K. Molecular docking and molecular dynamics simulation studies on PLCE1 encoded protein. J. Mol. Struct., 2019, 1198, 126936.
[http://dx.doi.org/10.1016/j.molstruc.2019.126936]
[150]
Dash, R.; Choi, H.J.; Moon, I.S. Mechanistic insights into the deleterious roles of Nasu-Hakola disease associated TREM2 variants. Sci. Rep., 2020, 10(1), 3663.
[http://dx.doi.org/10.1038/s41598-020-60561-x ] [PMID: 32107424]
[151]
Mohammad, T.; Siddiqui, S.; Shamsi, A.; Alajmi, M.F.; Hussain, A.; Islam, A.; Ahmad, F.; Hassan, M.I. Virtual screening approach to identify high-affinity inhibitors of serum and glucocorticoid-regulated kinase 1 among bioactive natural products: combined molecular docking and simulation studies. Molecules, 2020, 25(4), 823.
[http://dx.doi.org/10.3390/molecules25040823 ] [PMID: 32070031]
[152]
Muhseen, Z.T.; Li, G. Promising terpenes as natural antagonists of cancer: An in-silico approach. Molecules, 2019, 25(1), 155.
[http://dx.doi.org/10.3390/molecules25010155 ] [PMID: 31906032]
[153]
Gokhale, P.; Chauhan, A.P.S.; Arora, A.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation, 2019, 15(2), 104-115.
[http://dx.doi.org/10.6026/97320630015104 ] [PMID: 31435156]
[154]
Shukla, P.; Khandelwal, R.; Sharma, D.; Dhar, A.; Nayarisseri, A.; Singh, S.K. Virtual screening of il-6 inhibitors for idiopathic arthritis. Bioinformation, 2019, 15(2), 121-130.
[http://dx.doi.org/10.6026/97320630015121 ] [PMID: 31435158]
[155]
Udhwani, T.; Mukherjee, S.; Sharma, K.; Sweta, J.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. Design of PD-L1 inhibitors for lung cancer. Bioinformation, 2019, 15(2), 139-150.
[http://dx.doi.org/10.6026/97320630015139 ] [PMID: 31435160]
[156]
Rao, D.M.; Nayarisseri, A.; Yadav, M.; Patel, D. Comparative modeling of methylentetrahydrofolate reductase (MTHFR) enzyme and its mutational assessment: in silico approach. IJBBBS, 2010, 2(1), 5-9.
[http://dx.doi.org/10.9735/0975-3087.2.1.5-9]
[157]
Kelotra, S.; Jain, M.; Kelotra, A.; Jain, I.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. An in silico appraisal to identify high affinity anti-apoptotic synthetic tetrapeptide inhibitors targeting the mammalian caspase 3 enzyme. Asian Pac. J. Cancer Prev., 2014, 15(23), 10137-10142.
[http://dx.doi.org/10.7314/APJCP.2014.15.23.10137 ] [PMID: 25556438]
[158]
Gutlapalli, V.R.; Sykam, A.; Nayarisseri, A.; Suneetha, S.; Suneetha, L.M. Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs. Bioinformation, 2015, 11(12), 517-524.
[http://dx.doi.org/10.6026/97320630011517 ] [PMID: 26770024]
[159]
Nayarisseri, A.; Yadav, M.; Wishard, R. Computational evaluation of new homologous down regulators of Translationally Controlled Tumor Protein (TCTP) targeted for tumor reversion. Interdiscip. Sci., 2013, 5(4), 274-279.
[http://dx.doi.org/10.1007/s12539-013-0183-8 ] [PMID: 24402820]
[160]
Praseetha, S.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Pharmacological analysis of vorinostat analogues as potential anti-tumor agents targeting human histone deacetylases: an epigenetic treatment stratagem for cancers. Asian Pac. J. Cancer Prev., 2016, 17(3), 1571-1576.
[http://dx.doi.org/10.7314/APJCP.2016.17.3.1571 ] [PMID: 27039807]
[161]
Majhi, M.; Ali, M.A.; Limaye, A.; Sinha, K.; Bairagi, P.; Chouksey, M.; Shukla, R.; Kanwar, N.; Hussain, T.; Nayarisseri, A.; Singh, S.K. An in silico investigation of potential egfr inhibitors for the clinical treatment of colorectal cancer. Curr. Top. Med. Chem., 2018, 18(27), 2355-2366.
[http://dx.doi.org/10.2174/1568026619666181129144107 ] [PMID: 30499396]
[162]
Sharma, K.; Patidar, K.; Ali, M.A.; Patil, P.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high affinity compounds as potent vegfr2 inhibitors for the treatment of renal cell carcinoma. Curr. Top. Med. Chem., 2018, 18(25), 2174-2185.
[http://dx.doi.org/10.2174/1568026619666181130142237 ] [PMID: 30499413]
[163]
Shameer, K.; Nayarisseri, A.; Romero Duran, F.X.; González-Díaz, H. Improving neuropharmacology using big data, machine learning and computational algorithms. Curr. Neuropharmacol., 2017, 15(8), 1058-1061.
[http://dx.doi.org/10.2174/1570159X1508171114113425 ] [PMID: 29199918]
[164]
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic Issue: Chemoinformatics models for pharmaceutical design, part 2). Curr. Pharm. Des., 2016, 22(34), 5177-5178.
[http://dx.doi.org/10.2174/138161282234161110222751 ] [PMID: 27852211]
[165]
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial (Thematic Issue: Chemoinformatics models for pharmaceutical design, part 1). Curr. Pharm. Des., 2016, 22(33), 5041-5042.
[http://dx.doi.org/10.2174/138161282233161109224932 ] [PMID: 27852204]
[166]
Kelotra, A.; Gokhale, S.M.; Kelotra, S.; Mukadam, V.; Nagwanshi, K.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. Alkyloxy carbonyl modified hexapeptides as a high affinity compounds for Wnt5A protein in the treatment of psoriasis. Bioinformation, 2014, 10(12), 743-749.
[http://dx.doi.org/10.6026/97320630010743 ] [PMID: 25670877]
[167]
Lemkul, J.A.; Allen, W.J.; Bevan, D.R. Practical considerations for building GROMOS-compatible small-molecule topologies. J. Chem. Inf. Model., 2010, 50(12), 2221-2235.
[http://dx.doi.org/10.1021/ci100335w ] [PMID: 21117688]
[168]
Swaminathan, S.; Ravishanker, G.; Beveridge, D.L. Molecular dynamics of B-DNA including water and counterions: a 140-ps trajectory for d(CGCGAATTCGCG) based on the GROMOS force field. J. Am. Chem. Soc., 1991, 113(13), 5027-5040.
[http://dx.doi.org/10.1021/ja00013a043]
[169]
Schmid, N.; Eichenberger, A.P.; Choutko, A.; Riniker, S.; Winger, M.; Mark, A.E.; van Gunsteren, W.F. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur. Biophys. J., 2011, 40(7), 843-856.
[http://dx.doi.org/10.1007/s00249-011-0700-9 ] [PMID: 21533652]
[170]
Hands, M.D.; Slipchenko, L.V. Intermolecular interactions in complex liquids: effective fragment potential investigation of water-tert-butanol mixtures. J. Phys. Chem. B, 2012, 116(9), 2775-2786.
[http://dx.doi.org/10.1021/jp2077566 ] [PMID: 22324786]
[171]
Sudhamalla, B.; Gokara, M.; Ahalawat, N.; Amooru, D.G.; Subramanyam, R. Molecular dynamics simulation and binding studies of β-sitosterol with human serum albumin and its biological relevance. J. Phys. Chem. B, 2010, 114(27), 9054-9062.
[http://dx.doi.org/10.1021/jp102730p ] [PMID: 20565066]
[172]
Dong, C.; Li, X.; Guo, Z.; Qi, J. Development of a model for the rational design of molecular imprinted polymer: computational approach for combined molecular dynamics/quantum mechanics calculations. Anal. Chim. Acta, 2009, 647(1), 117-124.
[http://dx.doi.org/10.1016/j.aca.2009.05.040 ] [PMID: 19576395]
[173]
Pereira, G.R.C.; Da Silva, A.N.R.; Do Nascimento, S.S.; De Mesquita, J.F. In silico analysis and molecular dynamics simulation of human superoxide dismutase 3 (SOD3) genetic variants. J. Cell. Biochem., 2019, 120(3), 3583-3598.
[http://dx.doi.org/10.1002/jcb.27636 ] [PMID: 30206983]
[174]
Euston, S.R. Molecular dynamics simulation of protein adsorption at fluid interfaces: a comparison of all-atom and coarse-grained models. Biomacromolecules, 2010, 11(10), 2781-2787.
[http://dx.doi.org/10.1021/bm100857k ] [PMID: 20839848]
[175]
Neiss, C.; Saalfrank, P. Molecular dynamics simulation of the LOV2 domain from Adiantum capillus-veneris. J. Chem. Inf. Comput. Sci., 2004, 44(5), 1788-1793.
[http://dx.doi.org/10.1021/ci049883u ] [PMID: 15446837]
[176]
Verma, S.; Singh, A.; Mishra, A. Complex disruption effect of natural polyphenols on Bcl-2-Bax: molecular dynamics simulation and essential dynamics study. J. Biomol. Struct. Dyn., 2015, 33(5), 1094-1106.
[http://dx.doi.org/10.1080/07391102.2014.931823 ] [PMID: 24903407]
[177]
Zeb, A.; Park, C.; Rampogu, S.; Son, M.; Lee, G.; Lee, K.W. Structure-based drug designing recommends HDAC6 inhibitors to attenuate microtubule-associated Tau-pathogenesis. ACS Chem. Neurosci., 2019, 10(3), 1326-1335.
[http://dx.doi.org/10.1021/acschemneuro.8b00405 ] [PMID: 30407786]
[178]
Ul Haq, F.; Abro, A.; Raza, S.; Liedl, K.R.; Azam, S.S. Molecular dynamics simulation studies of novel β-lactamase inhibitor. J. Mol. Graph. Model., 2017, 74, 143-152.
[http://dx.doi.org/10.1016/j.jmgm.2017.03.002 ] [PMID: 28432959]
[179]
Malleda, C.; Ahalawat, N.; Gokara, M.; Subramanyam, R. Molecular dynamics simulation studies of betulinic acid with human serum albumin. J. Mol. Model., 2012, 18(6), 2589-2597.
[http://dx.doi.org/10.1007/s00894-011-1287-x ] [PMID: 22076062]
[180]
Rather, M.A.; Dutta, S.; Guttula, P.K.; Dhandare, B.C.; Yusufzai, S.I.; Zafar, M.I. Structural analysis, molecular docking and molecular dynamics simulations of G-protein-coupled receptor (kisspeptin) in fish. J. Biomol. Struct. Dyn., 2020, 38(8), 2422-2439.
[http://dx.doi.org/10.1080/07391102.2019.1633407 ] [PMID: 31208300]
[181]
Fatima, N.; Kalsoom, A.; Mumtaz, A.; Muhammad, S.A. Computational drug designing of fungal pigments as potential aromatase inhibitors. |||. Bangladesh J. Pharmacol., 2014, 9(4), 575-579.
[http://dx.doi.org/10.3329/bjp.v9i4.20435]
[182]
Roccatano, D.; Sbardella, G.; Aschi, M.; Amicosante, G.; Bossa, C.; Di Nola, A.; Mazza, F. Dynamical aspects of TEM-1 β-lactamase probed by molecular dynamics. J. Comput. Aided Mol. Des., 2005, 19(5), 329-340.
[http://dx.doi.org/10.1007/s10822-005-7003-0 ] [PMID: 16184435]
[183]
Berendsen, H.J.; van der Spoel, D.; van Drunen, R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput. Phys. Commun., 1995, 91(1-3), 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[184]
Abraham, M.J.; Gready, J.E. Optimization of parameters for molecular dynamics simulation using smooth particle-mesh Ewald in GROMACS 4.5. J. Comput. Chem., 2011, 32(9), 2031-2040.
[http://dx.doi.org/10.1002/jcc.21773 ] [PMID: 21469158]
[185]
van der Spoel, D.; van Maaren, P.J.; Caleman, C. GROMACS molecule & liquid database. Bioinformatics, 2012, 28(5), 752-753.
[http://dx.doi.org/10.1093/bioinformatics/bts020 ] [PMID: 22238269]
[186]
Chandrakar, B.; Jain, A.; Roy, S.; Gutlapalli, V.R.; Saraf, S.; Suppahia, A.; Verma, A.; Tiwari, A.; Yadav, M.; Nayarisseri, A. Molecular modeling of Acetyl-CoA carboxylase (ACC) from Jatropha curcas and virtual screening for identification of inhibitors. J. Pharm. Res., 2013, 6(9), 913-918.
[187]
Nayarisseri, A.; Singh, S.K. Functional Inhibition of VEGF and EGFR Suppressors in Cancer Treatment. Curr. Top. Med. Chem., 2019, 19(3), 178-179.
[http://dx.doi.org/10.2174/156802661903190328155731 ] [PMID: 30950335]
[188]
Monteiro, A.F.M.; Viana, J.O.; Nayarisseri, A.; Zondegoumba, E.N.; Mendonça, Junior, F.J.B.; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against Alzheimer’s and Parkinson’s diseases. Oxid. Med. Cell. Longev., 2018, 2018, 7912765.
[http://dx.doi.org/10.1155/2018/7912765 ] [PMID: 30693065]
[189]
Patidar, K.; Panwar, U.; Vuree, S.; Sweta, J.; Sandhu, M.K.; Nayarisseri, A.; Singh, S.K. An in silico approach to identify high affinity small molecule targeting m-tor inhibitors for the clinical treatment of breast cancer. Asian Pac. J. Cancer Prev., 2019, 20(4), 1229-1241.
[http://dx.doi.org/10.31557/APJCP.2019.20.4.1229 ] [PMID: 31030499]
[190]
Nayarisseri, A. Experimental and computational approaches to improve binding affinity in chemical biology and drug discovery. Curr. Top. Med. Chem., 20(19), 1651-1956.
[191]
Prajapati, L.; Khandelwal, R.; Yogalakshmi, K.N.; Munshi, A.; Nayarisseri, A. Computer-aided Structure prediction of Bluetongue Virus coat protein VP2 assisted by Optimized Potential for Liquid Simulations(OPLS). Curr. Top. Med. Chem., 2020, 20(19), 1716-1728.
[PMID: 32416694]
[192]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7, 42717.
[http://dx.doi.org/10.1038/srep42717 ] [PMID: 28256516]
[193]
Ndombera, F.T.; Maiyoh, G.K.; Tuei, V.C. Pharmacokinetic, physicochemical and medicinal properties of n-glycoside anti-cancer agent more potent than 2-deoxy-d-glucose in lung cancer cells. J. Pharm. Pharmacol., 2019, 7, 165-176.
[194]
Ferreira, L.L.G.; Andricopulo, A.D. ADMET modeling approaches in drug discovery. Drug Discov. Today, 2019, 24(5), 1157-1165.
[http://dx.doi.org/10.1016/j.drudis.2019.03.015 ] [PMID: 30890362]
[195]
Jabir, N.R.; Shakil, S.; Tabrez, S.; Khan, M.S.; Rehman, M.T.; Ahmed, B.A. In silico screening of glycogen synthase kinase-3β targeted ligands against acetylcholinesterase and its probable relevance to Alzheimer’s disease. J. Biomol. Struct. Dyn., 2020, 1-10.
[http://dx.doi.org/10.1080/07391102.2020.1784796 ] [PMID: 32588759]

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