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Sinha, K.; Majhi, M.; Thakur, G.; Patidar, K.; Sweta, J.; Nayarisseri, A.; Singh, S.K. Computer aided drug designing for the identification of high affinity small molecule targeting CD20 for the clinical treatment of Chronic Lymphocytic Leukemia (CLL). Curr. Top. Med. Chem., 2018, 18(29), 2527-2542.
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Patidar, K.; Deshmukh, A.; Bandaru, S.; Lakkaraju, C.; Girdhar, A.; Vr, G.; 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.
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Khandelwal, R.; Chauhan, A.P.S.; Hussain, T.; Hood, E.A.; Nayarisseri, A. 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.
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Sharda, S.; Sarmandal, P.; Cherukommu, S.; Dindhoria, K.; Yadav, M.; Bandaru, S.; Nayarisseri, A. 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.
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Bandaru, S.; Alvala, M.; Akka, J.; Sagurthi, S.R.; Nayarisseri, A.; Kumar Singh, S.; Prasad, M.H. 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.
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Sinha, C.; Nischal, A.; Bandaru, S.; Kasera, P.; Rajput, A.; Nayarisseri, A.; Khattri, S. An in silico approach for identification of novel inhibitors as a potential therapeutics targeting HIV-1 viral infectivity factor. Curr. Top. Med. Chem., 2015, 15(1), 65-72.
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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.
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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.
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Dunna, N.R.; Bandaru, S.; Raj Akare, U.; Rajadhyax, S.; Ravi Gutlapalli, V.; Yadav, M.; Nayarisseri, A. Multiclass comparative virtual screening to identify novel Hsp90 inhibitors: a therapeutic breast cancer drug target. Curr. Top. Med. Chem., 2015, 15(1), 57-64.
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Bandaru, S.; Ponnala, D.; Lakkaraju, C.; Kumar, C.; Bhukya, U.S.; 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., 2014, 16, 3759-3765.
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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.
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Bandaru, S.; Hema Prasad, M.; Jyothy, A.; Nayarisseri, A.; Yadav, M. Binding Modes and Pharmacophoric Features of Muscarinic Antagonism and β2 Agonism (MABA) Conjugates. Curr. Top. Med. Chem., 2013, 13(14), 1650-1655.
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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. Pharma Res., 2013, 7(2), 150-156.
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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.
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Vuree, S.; Dunna, N.R.; Khan, I.A.; Alharbi, K.K.; Vishnupriya, S.; Soni, D.; Shah, P.; Chandok, H.; Yadav, M.; Nayarisseri, A. Pharmacogenomics of drug resistance in Breast Cancer Resistance Protein (BCRP) and its mutated variants. J. Pharm. Res., 2013, 6(7), 791-798.
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Gudala, S.; Khan, U.; Kanungo, N.; Bandaru, S.; Hussain, T.; Parihar, M.L.; 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, 8191-8196.
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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.
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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. Curr. Top. Med. Chem., 2016, 64, 64-73.
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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.
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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.
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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.
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Nasr, A.B.; Ponnala, D.; Sagurthi, S.R.; Kattamuri, R.K.; Marri, V.K.; Gudala, S.; Nayarisseri, A. Molecular Docking studies of FKBP12-mTOR inhibitors using binding predictions. Bioinformation, 2015, 11(6), 307-315.
[29]
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
[30]
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.
[31]
Monteiro, A.F.M.; Viana, J.D.O.; Nayarisseri, A.; Zondegoumba, E.N.; Junior, F.J.B.M.; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against alzheimer’s and parkinson’s diseases. Oxid. Med. Cell. Longev., 2018, 2018, 21.
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Nayarisseri, A.; Hood, E.A. Advancement in microbial cheminformatics. Curr. Top. Med. Chem., 2018, 18(29), 2459-2461.
[33]
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.
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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.
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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.
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Rao, D.M.; Nayarisseri, A.; Yadav, M.; Patel, D. Comparative modeling of methylentetrahydrofolate reductase (MTHFR) enzyme and its mutational assessment: In silico approach. Int. J. Bioinform. Res, 2010, 2(1), 5-9.
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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.
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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.
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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.
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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.
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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.
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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.
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Shameer, K.; Nayarisseri, A.; Duran, F.X.R.; González-Díaz, H. Improving neuropharmacology using big data, machine learning and computational algorithms. Curr. Neuropharmacol., 2017, 15(8), 1058-1061.
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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.
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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.
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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.
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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.
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Nayarisseri, A.; Singh, S.K. Functional inhibition of VEGF and EGFR suppressors in cancer treatment. Curr. Top. Med. Chem., 2019, 19(3), 178-179.
[49]
Monteiro, A.F.M.; Viana, J.D.O.; Nayarisseri, A.; Zondegoumba, E.N.; Junior, F.J.B.M.; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against alzheimer’s and parkinson’s diseases. Oxid. Med. Cell. Longev., 2018, 20187912765
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Marques, K.M.R.; do Desterro, M.R.; de Arruda, S.M.; de Araújo Neto, L.N.; de Lima, M.d.C.A.; de Almeida, S.M.V.; da Silva, E.C.D.; de Aquino, T.M.; da Silva-Júnior, E.F.; de Araújo-Júnior, J.X.; Silva, M.d.M.; Dantas, M.D.d.A.; Santos, J.C.C.; Figueiredo, I.M.; Bazin, M-A.; Marchand, P.; da Silva, T.G.; Mendonça, Junior, F.J.B. 5-Nitro-thiophene-thiosemicarbazone derivatives present antitumor activity mediated by apoptosis and dna intercalation. Curr. Top. Med. Chem., 2019, 19(13), 1074-1091.
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Azimi, F.; Ghasemi, J.B.; Saghaei, L.; Hassanzadeh, F.; Mahdavi, M.; Sadeghi-aliabadi, H.; Scotti, M.T.; Scotti, L. Identification of essential 2D and 3D chemical features for discovery of the novel tubulin polymerization inhibitors. Curr. Top. Med. Chem., 2019, 19(13), 1092-1120.
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Tugcu, G.; Sipahi, H.; Aydın, A. Application of a validated QSTR model for repurposing cox-2 inhibitor coumarin derivatives as potential antitumor agents. Curr. Top. Med. Chem., 2019, 19(13), 1121-1128.
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Sharda, S.; Khandelwal, R.; Adhikary, R.; Sharma, D.; Majhi, M.; Hussain, T.; Nayarisseri, A.; Singh, S.K. A computer-aided drug designing for pharmacological inhibition of mutant alk for the treatment of non-small cell lung cancer. Curr. Top. Med. Chem., 2019, 19(13), 1129-1144.
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Kartsev, V.; Geronikaki, A.; Petrou, A.; Lichitsky, B.; Sirakanyan, S.; Kostic, M.; Smiljkovic, M.; Soković, M. Griseofulvin derivatives: Synthesis, molecular docking and biological evaluation. Curr. Top. Med. Chem., 2019, 19(13), 1145-1161.
Call for Papers in Thematic Issues
24 September, 2024
Chemistry Based on Natural Products for Therapeutic Purposes
The development of new pharmaceuticals for a wide range of medical conditions has long relied on the identification of promising natural products (NPs). There are over sixty percent of cancer, infectious illness, and CNS disease medications that include an NP pharmacophore, according to the Food and Drug Administration. Since NP ...read more
28 July, 2024
Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-Aided Drug Design
Drug development discovery has faced several challenges over the years. In fact, the evolution of classical approaches to modern methods using computational methods, or Computer-Aided Drug Design (CADD), has shown promising and essential results in any drug discovery campaign. Among these methods, molecular docking is one of the most notable ...read more
13 August, 2024
Drug Discovery in the Age of Artificial Intelligence
In the age of artificial intelligence (AI), we have witnessed a significant boom in AI techniques for drug discovery. AI techniques are increasingly integrated and accelerating the drug discovery process. These developments have not only attracted the attention of academia and industry but also raised important questions regarding the selection ...read more
31 December, 2024
From Biodiversity to Chemical Diversity: Focus of Flavonoids
Flavonoids are the largest group of polyphenols, plant secondary metabolites arising from the essential aromatic amino acid phenylalanine (or more rarely from tyrosine) via the phenylpropanoid pathway. The flavan nucleus is the basic 15-carbon skeleton of flavonoids (C6-C3-C6), which consists of two phenyl rings (A and B) and a heterocyclic ...read more
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