Login Register Cart 0
Review Article

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery

Author(s): Anuraj Nayarisseri*, Ravina Khandelwal, Poonam Tanwar, Maddala Madhavi, Diksha Sharma, Garima Thakur, Alejandro Speck-Planche and Sanjeev Kumar Singh*

Volume 22, Issue 6, 2021

Published on: 04 January, 2021

Page: [631 - 655] Pages: 25

DOI: 10.2174/1389450122999210104205732

Price: $65

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.

Keywords: Machine learning, artificial intelligence, big data, virtual screening, precision medicine, drug discovery.

« Previous Next »
Graphical Abstract

[1]
Boudellioua I, Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R. DeepPVP: phenotype-based prioritization of causative variants using deep learning. BMC Bioinformatics 2019; 20(1): 65.
[http://dx.doi.org/10.1186/s12859-019-2633-8] [PMID: 30727941]
[2]
Eilbeck K, Quinlan A, Yandell M. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet 2017; 18(10): 599-612.
[http://dx.doi.org/10.1038/nrg.2017.52] [PMID: 28804138]
[3]
Bolger AM, Poorter H, Dumschott K, et al. Computational aspects underlying genome to phenome analysis in plants. Plant J 2019; 97(1): 182-98.
[http://dx.doi.org/10.1111/tpj.14179] [PMID: 30500991]
[4]
Matukumalli LK, Grefenstette JJ, Hyten DL, Choi IY, Cregan PB, Van Tassell CP. Application of machine learning in SNP discovery. BMC Bioinformatics 2006; 7(1): 4.
[http://dx.doi.org/10.1186/1471-2105-7-4] [PMID: 16398931]
[5]
Romagnoni A, Jégou S, Van Steen K, Wainrib G, Hugot JP. International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data. Sci Rep 2019; 9(1): 10351.
[http://dx.doi.org/10.1038/s41598-019-46649-z] [PMID: 31316157]
[6]
Abraham G, Kowalczyk A, Zobel J, Inouye M. Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease. Genet Epidemiol 2013; 37(2): 184-95.
[http://dx.doi.org/10.1002/gepi.21698] [PMID: 23203348]
[7]
Szymczak S, Biernacka JM, Cordell HJ, et al. Machine learning in genome-wide association studies. Genet Epidemiol 2009; 33(S1)(Suppl. 1): S51-7.
[http://dx.doi.org/10.1002/gepi.20473] [PMID: 19924717]
[8]
Lin HY, Chen YA, Tsai YY, Qu X, Tseng TS, Park JY. TRM: a powerful two-stage machine learning approach for identifying SNP-SNP interactions. Ann Hum Genet 2012; 76(1): 53-62.
[http://dx.doi.org/10.1111/j.1469-1809.2011.00692.x] [PMID: 22150548]
[9]
Tomita Y, Tomida S, Hasegawa Y, et al. Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma. BMC Bioinformatics 2004; 5(1): 120.
[http://dx.doi.org/10.1186/1471-2105-5-120] [PMID: 15339344]
[10]
Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov 2019; 18(1): 41-58.
[http://dx.doi.org/10.1038/nrd.2018.168] [PMID: 30310233]
[11]
Barratt MJ, Frail DE. Drug repositioning: Bringing new life to shelved assets and existing drugs. John Wiley & Sons 2012.
[http://dx.doi.org/10.1002/9781118274408]
[12]
Cha Y, Erez T, Reynolds IJ, et al. Drug repurposing from the perspective of pharmaceutical companies. Br J Pharmacol 2018; 175(2): 168-80.
[http://dx.doi.org/10.1111/bph.13798] [PMID: 28369768]
[13]
Shi W, Chen X, Deng L. A Review of Recent Developments and Progress in computational drug repositioning. Curr Pharm Des 2020; 26(26): 3059-68.
[http://dx.doi.org/10.2174/1381612826666200116145559] [PMID: 31951162]
[14]
Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 2019; 18(5): 435-41.
[http://dx.doi.org/10.1038/s41563-019-0338-z] [PMID: 31000803]
[15]
Zhang L, Tan J, Han D, Zhu H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today 2017; 22(11): 1680-5.
[http://dx.doi.org/10.1016/j.drudis.2017.08.010] [PMID: 28881183]
[16]
Zhao K, So HC. Using drug expression profiles and machine learning approach for drug repurposingComputational Methods for Drug Repurposing. New York, NY: Humana Press 2019; pp. 219-37.
[17]
Park K. A review of computational drug repurposing. Transl Clin Pharmacol 2019; 27(2): 59-63.
[http://dx.doi.org/10.12793/tcp.2019.27.2.59] [PMID: 32055582]
[18]
Koromina M, Pandi MT, Patrinos GP. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. OMICS 2019; 23(11): 539-48.
[http://dx.doi.org/10.1089/omi.2019.0151] [PMID: 31651216]
[19]
Zhao K, So HC. 2017.
[20]
Kim E, Choi AS, Nam H. Drug repositioning of herbal compounds via a machine-learning approach. BMC Bioinformatics 2019; 20(10)(Suppl. 10): 247.
[http://dx.doi.org/10.1186/s12859-019-2811-8] [PMID: 31138103]
[21]
Yella JK, Yaddanapudi S, Wang Y, Jegga AG. Changing trends in computational drug repositioning. Pharmaceuticals (Basel) 2018; 11(2): 57.
[http://dx.doi.org/10.3390/ph11020057] [PMID: 29874824]
[22]
Jiang HJ, Huang YA, You ZH. Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder. BioMed Res Int 2019; 20192426958
[http://dx.doi.org/10.1155/2019/2426958] [PMID: 31534955]
[23]
Waszkowycz B. Towards improving compound selection in structure-based virtual screening. Drug Discov Today 2008; 13(5-6): 219-26.
[http://dx.doi.org/10.1016/j.drudis.2007.12.002] [PMID: 18342797]
[24]
Li H, Yap CW, Ung CY, et al. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J Pharm Sci 2007; 96(11): 2838-60.
[http://dx.doi.org/10.1002/jps.20985] [PMID: 17786989]
[25]
Limaye A, Sweta J, Madhavi M, et al. In Silico Insights on GD2: A Potential Target for Pediatric Neuroblastoma. Curr Top Med Chem 2019; 19(30): 2766-81.
[http://dx.doi.org/10.2174/1568026619666191112115333] [PMID: 31721713]
[26]
Sinha K, Majhi M, Thakur G, et al. 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): 2527-42.
[http://dx.doi.org/10.2174/1568026619666181210150044] [PMID: 30526461]
[27]
Nayarisseri A. Prospects of utilizing computational techniques for the treatment of Human diseases. Curr Top Med Chem 2019; 19(13): 1071-4.
[http://dx.doi.org/10.2174/156802661913190827102426] [PMID: 31490742]
[28]
Bandaru S, Sumithnath TG, Sharda S, et al. Helix-coil transition signatures b-raf v600e mutation and virtual screening for inhibitors directed against mutant B-Raf. Curr Drug Metab 2017; 18(6): 527-34.
[http://dx.doi.org/10.2174/1389200218666170503114611] [PMID: 28472910]
[29]
Nayarisseri A. Most promising compounds for treating COVID-19 and recent trends in antimicrobial & antifungal agents. Curr Top Med Chem 2020; 20(24): 2119-25.
[http://dx.doi.org/10.2174/156802662023201001094634] [PMID: 33153418]
[30]
Kleandrova VV, Scotti MT, Scotti L, Nayarisseri A, Speck-Planche A. Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR QSAR Environ Res 2020; 31(11): 815-36.
[http://dx.doi.org/10.1080/1062936X.2020.1818617] [PMID: 32967475]
[31]
Nayarisseri A. Experimental and computational approaches to improve binding affinity in chemical biology and drug discovery. Curr Top Med Chem 2020; 20(19): 1651-60.
[http://dx.doi.org/10.2174/156802662019200701164759] [PMID: 32614747]
[32]
Nayarisseri A, Khandelwal R, Madhavi M, et al. Shape-based machine learning models for the potential novel COVID-19 protease inhibitors assisted by molecular dynamics simulation. Curr Top Med Chem 2020; 20(24): 2146-67.
[http://dx.doi.org/10.2174/1568026620666200704135327] [PMID: 32621718]
[33]
Prajapati L, Khandelwal R, Yogalakshmi KN, 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): 1720-32.
[http://dx.doi.org/10.2174/1568026620666200516153753] [PMID: 32416694]
[34]
Cross JB, Thompson DC, Rai BK, et al. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 2009; 49(6): 1455-74.
[http://dx.doi.org/10.1021/ci900056c] [PMID: 19476350]
[35]
Pirhadi S, Ghasemi JB. Pharmacophore identification, molecular docking, virtual screening, and in silico ADME studies of non‐nucleoside reverse transcriptase inhibitors. Mol Inform 2012; 31(11-12): 856-66.
[http://dx.doi.org/10.1002/minf.201200018] [PMID: 27476739]
[36]
Akare UR, Bandaru S, Shaheen U, et al. Molecular docking approaches in identification of High affinity inhibitors of Human SMO receptor. Bioinformation 2014; 10(12): 737-42.
[http://dx.doi.org/10.6026/97320630010737] [PMID: 25670876]
[37]
Bandaru S, Alvala M, Akka J, et al. 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): 5221-33.
[http://dx.doi.org/10.2174/1381612822666160513145721] [PMID: 27174812]
[38]
Ali MA, Vuree S, Goud H, Hussain T, Nayarisseri A, Singh SK. Identification of High-affinity Small Molecules Targeting Gamma Secretase for the Treatment of Alzheimer’s Disease. Curr Top Med Chem 2019; 19(13): 1173-87.
[http://dx.doi.org/10.2174/1568026619666190617155326] [PMID: 31244427]
[39]
Hevener KE, Zhao W, Ball DM, et al. Validation of molecular docking programs for virtual screening against dihydropteroate synthase. J Chem Inf Model 2009; 49(2): 444-60.
[http://dx.doi.org/10.1021/ci800293n] [PMID: 19434845]
[40]
Nayarisseri A, Moghni SM, Yadav M, et al. In silico investigations on HSP90 and its inhibition for the therapeutic prevention of breast cancer. J Pharm Res 2013; 7(2): 150-6.
[http://dx.doi.org/10.1016/j.jopr.2013.02.020]
[41]
Ma DL, Chan DSH, Leung CH. Molecular docking for virtual screening of natural product databases. Chem Sci (Camb) 2011; 2(9): 1656-65.
[http://dx.doi.org/10.1039/C1SC00152C]
[42]
Gudala S, Khan U, Kanungo N, et al. 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): 8191-6.
[http://dx.doi.org/10.7314/APJCP.2015.16.18.8191] [PMID: 26745059]
[43]
Zhang C, Li Q, Meng L, Ren Y. Design of novel dopamine D2 and serotonin 5-HT2A receptors dual antagonists toward schizophrenia: An integrated study with QSAR, molecular docking, virtual screening and molecular dynamics simulations. J Biomol Struct Dyn 2020; 38(3): 860-85.
[http://dx.doi.org/10.1080/07391102.2019.1590244] [PMID: 30916624]
[44]
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]
[45]
Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3(11): 935-49.
[http://dx.doi.org/10.1038/nrd1549] [PMID: 15520816]
[46]
Chen B, Harrison RF, Papadatos G, et al. Evaluation of machine-learning methods for ligand-based virtual screening. J Comput Aided Mol Des 2007; 21(1-3): 53-62.
[http://dx.doi.org/10.1007/s10822-006-9096-5] [PMID: 17205373]
[47]
Ma XH, Jia J, Zhu F, Xue Y, Li ZR, Chen YZ. Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries. Comb Chem High Throughput Screen 2009; 12(4): 344-57.
[http://dx.doi.org/10.2174/138620709788167944] [PMID: 19442064]
[48]
Rester U. From virtuality to reality - Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr Opin Drug Discov Devel 2008; 11(4): 559-68.
[PMID: 18600572]
[49]
Lešnik S, Štular T, Brus B, et al. LiSiCA: a software for ligand-based virtual screening and its application for the discovery of butyrylcholinesterase inhibitors. J Chem Inf Model 2015; 55(8): 1521-8.
[http://dx.doi.org/10.1021/acs.jcim.5b00136] [PMID: 26158767]
[50]
Afantitis A, Melagraki G, Koutentis PA, Sarimveis H, Kollias G. Ligand-based virtual screening procedure for the prediction and the identification of novel β-amyloid aggregation inhibitors using Kohonen maps and Counterpropagation Artificial Neural Networks. Eur J Med Chem 2011; 46(2): 497-508.
[http://dx.doi.org/10.1016/j.ejmech.2010.11.029] [PMID: 21167625]
[51]
Cho Y, Ioerger TR, Sacchettini JC. Discovery of novel nitrobenzothiazole inhibitors for Mycobacterium tuberculosis ATP phosphoribosyl transferase (HisG) through virtual screening. J Med Chem 2008; 51(19): 5984-92.
[http://dx.doi.org/10.1021/jm800328v] [PMID: 18778048]
[52]
Ekins S, Godbole AA, Kéri G, et al. Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I. Tuberculosis (Edinb) 2017; 103: 52-60.
[http://dx.doi.org/10.1016/j.tube.2017.01.005] [PMID: 28237034]
[53]
Jones G, Willett P, Glen RC, Leach AR, Taylor R. Development and validation of a genetic algorithm for flexible docking. J Mol Biol 1997; 267(3): 727-48.
[http://dx.doi.org/10.1006/jmbi.1996.0897] [PMID: 9126849]
[54]
Sahila MM, Babitha PP, Bandaru S, Nayarisseri A, Doss VA. Molecular docking based screening of GABA (A) receptor inhibitors from plant derivatives. Bioinformation 2015; 11(6): 280-9.
[http://dx.doi.org/10.6026/97320630011280] [PMID: 26229288]
[55]
Lu SH, Wu JW, Liu HL, et al. The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies. J Biomed Sci 2011; 18(1): 8.
[http://dx.doi.org/10.1186/1423-0127-18-8] [PMID: 21251245]
[56]
Sakkiah S, Thangapandian S, John S, Kwon YJ, Lee KW. 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. Eur J Med Chem 2010; 45(6): 2132-40.
[http://dx.doi.org/10.1016/j.ejmech.2010.01.016] [PMID: 20206418]
[57]
Rampogu S, Son M, Park C, Kim HH, Suh JK, Lee KW. Sulfonanilide derivatives in identifying novel aromatase inhibitors by applying docking, virtual screening, and MD simulations studies. BioMed Res Int 2017; 20172105610
[http://dx.doi.org/10.1155/2017/2105610] [PMID: 29312992]
[58]
Jacob RB, Andersen T, McDougal OM. Accessible high-throughput virtual screening molecular docking software for students and educators. PLOS Comput Biol 2012; 8(5)e1002499
[http://dx.doi.org/10.1371/journal.pcbi.1002499] [PMID: 22693435]
[59]
Jain D, Udhwani T, Sharma S, et al. 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]
[60]
Mendonça-Junior FJB, Scotti MT, Nayarisseri A, Zondegoumba ENT, 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]
[61]
Nayarisseri A, Hood EA. ADVANCEMENT IN MICROBIAL CHEMINFORMATICS. Curr Top Med Chem 2018; 18(29): 2459-61.
[http://dx.doi.org/10.2174/1568026619666181120121528] [PMID: 30457050]
[62]
Gokhale P, Chauhan APS, Arora A, Khandekar N, Nayarisseri A, Singh SK. FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation 2019; 15(2): 104-15.
[http://dx.doi.org/10.6026/97320630015104] [PMID: 31435156]
[63]
Shukla P, Khandelwal R, Sharma D, Dhar A, Nayarisseri A, Singh SK. Virtual Screening of IL-6 Inhibitors for Idiopathic Arthritis. Bioinformation 2019; 15(2): 121-30.
[http://dx.doi.org/10.6026/97320630015121] [PMID: 31435158]
[64]
Udhwani T, Mukherjee S, Sharma K, et al. Design of PD-L1 inhibitors for lung cancer. Bioinformation 2019; 15(2): 139-50.
[http://dx.doi.org/10.6026/97320630015139] [PMID: 31435160]
[65]
Jain AN. Virtual screening in lead discovery and optimization. Curr Opin Drug Discov Devel 2004; 7(4): 396-403.
[PMID: 15338948]
[66]
Kelotra S, Jain M, Kelotra A, et al. 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): 10137-42.
[http://dx.doi.org/10.7314/APJCP.2014.15.23.10137] [PMID: 25556438]
[67]
Kramer B, Rarey M, Lengauer T. Evaluation of the FLEXX incremental construction algorithm for protein-ligand docking. Proteins 1999; 37(2): 228-41.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19991101)37:2<228:AID-PROT8>3.0.CO;2-8] [PMID: 10584068]
[68]
Sweta J, Khandelwal R, Srinitha S, et al. Identification of High-Affinity Small Molecule Targeting IDH2 for the Clinical Treatment of Acute Myeloid Leukemia. Asian Pac J Cancer Prev 2019; 20(8): 2287-97.
[http://dx.doi.org/10.31557/APJCP.2019.20.8.2287] [PMID: 31450897]
[69]
Gutlapalli VR, Sykam A, Nayarisseri A, Suneetha S, Suneetha LM. Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs. Bioinformation 2015; 11(12): 517-24.
[http://dx.doi.org/10.6026/97320630011517] [PMID: 26770024]
[70]
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): 274-9.
[http://dx.doi.org/10.1007/s12539-013-0183-8] [PMID: 24402820]
[71]
Pierri CL, Parisi G, Porcelli V. Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening. Biochim Biophys Acta 2010; 1804(9): 1695-712.
[http://dx.doi.org/10.1016/j.bbapap.2010.04.008] [PMID: 20433957]
[72]
Majhi M, Ali MA, Limaye A, et al. An in silico investigation of potential egfr inhibitors for the clinical treatment of colorectal cancer. Curr Top Med Chem 2018; 18(27): 2355-66.
[http://dx.doi.org/10.2174/1568026619666181129144107] [PMID: 30499396]
[73]
Sharma K, Patidar K, Ali MA, et al. 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): 2174-85.
[http://dx.doi.org/10.2174/1568026619666181130142237] [PMID: 30499413]
[74]
Shameer K, Nayarisseri A, Romero Duran FX, González-Díaz H. Improving neuropharmacology using big data, machine learning and computational algorithms. Curr Neuropharmacol 2017; 15(8): 1058-61.
[http://dx.doi.org/10.2174/1570159X1508171114113425] [PMID: 29199918]
[75]
Schneider G, Böhm HJ. Virtual screening and fast automated docking methods. Drug Discov Today 2002; 7(1): 64-70.
[http://dx.doi.org/10.1016/S1359-6446(01)02091-8] [PMID: 11790605]
[76]
Varnek A, Baskin I. Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J Chem Inf Model 2012; 52(6): 1413-37.
[http://dx.doi.org/10.1021/ci200409x] [PMID: 22582859]
[77]
Chiba S, Ikeda K, Ishida T, et al. Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Sci Rep 2015; 5: 17209.
[http://dx.doi.org/10.1038/srep17209] [PMID: 26607293]
[78]
Dahl GE, Jaitly N, Salakhutdinov R. 2014.
[79]
Joshi T, Mathpal S, Sharma P. Molecular Docking Study of drug molecules from Drug Bank database against COVID-19 Mpro protein 2020.
[80]
Liu Z, Du J, Fang J, Yin Y, Xu G, Xie L. Deep screening: a deep learning-based screening web server for accelerating drug discovery. Database 2019; 2019
[http://dx.doi.org/10.1093/database/baz104]]
[81]
Guevara L, Garcia Tsao G, Uscanga LF. A study with quinfamide in the treatment of chronic amebiasis in adults. Clin Ther 1983; 6(1): 43-6.
[PMID: 6673830]
[82]
Slighter RG, Yarinsky A, Drobeck HP, Bailey DM. Activity of quinfamide against natural infections of Entamoeba criceti in hamsters: a new potent agent for intestinal amoebiasis. Parasitology 1980; 81(1): 157-68.
[http://dx.doi.org/10.1017/S0031182000055128] [PMID: 6252530]
[83]
Baron BM, Harrison BL, McDonald IA, et al. Potent indole- and quinoline-containing N-methyl-D-aspartate antagonists acting at the strychnine-insensitive glycine binding site. J Pharmacol Exp Ther 1992; 262(3): 947-56.
[PMID: 1388205]
[84]
Millan MJ, Seguin L. Chemically-diverse ligands at the glycine B site coupled to N-methyl-D-aspartate (NMDA) receptors selectively block the late phase of formalin-induced pain in mice. Neurosci Lett 1994; 178(1): 139-43.
[http://dx.doi.org/10.1016/0304-3940(94)90309-3] [PMID: 7816323]
[85]
Bouvier NM, Palese P. The biology of influenza viruses. Vaccine 2008; 26(Suppl. 4): D49-53.
[http://dx.doi.org/10.1016/j.vaccine.2008.07.039] [PMID: 19230160]
[86]
Taylor G. Sialidases: structures, biological significance and therapeutic potential. Curr Opin Struct Biol 1996; 6(6): 830-7.
[http://dx.doi.org/10.1016/S0959-440X(96)80014-5] [PMID: 8994884]
[87]
Gao R, Cao B, Hu Y, et al. Human infection with a novel avian-origin influenza A (H7N9) virus. N Engl J Med 2013; 368(20): 1888-97.
[http://dx.doi.org/10.1056/NEJMoa1304459] [PMID: 23577628]
[88]
Hurt AC. The epidemiology and spread of drug resistant human influenza viruses. Curr Opin Virol 2014; 8: 22-9.
[http://dx.doi.org/10.1016/j.coviro.2014.04.009] [PMID: 24866471]
[89]
Bloom JD, Gong LI, Baltimore D. Permissive secondary mutations enable the evolution of influenza oseltamivir resistance. Science 2010; 328(5983): 1272-5.
[http://dx.doi.org/10.1126/science.1187816] [PMID: 20522774]
[90]
Zhang L, Ai HX, Li SM, et al. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function. Oncotarget 2017; 8(47): 83142-54.
[http://dx.doi.org/10.18632/oncotarget.20915] [PMID: 29137330]
[91]
Ashtawy HM, Mahapatra NR. Task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment. J Chem Inf Model 2018; 58(1): 119-33.
[http://dx.doi.org/10.1021/acs.jcim.7b00309] [PMID: 29190087]
[92]
Heinrich T, Seenisamy J, Blume B, et al. Discovery and structure-based optimization of next-generation reversible methionine Aminopeptidase-2 (MetAP-2) inhibitors. J Med Chem 2019; 62(10): 5025-39.
[http://dx.doi.org/10.1021/acs.jmedchem.9b00041] [PMID: 30939017]
[93]
Bekić SS, Marinović MA, Petri ET, et al. Identification of d-seco modified steroid derivatives with affinity for estrogen receptor α and β isoforms using a non-transcriptional fluorescent cell assay in yeast. Steroids 2018; 130: 22-30.
[http://dx.doi.org/10.1016/j.steroids.2017.12.002] [PMID: 29224741]
[94]
Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discov Today 2018; 23(8): 1538-46.
[http://dx.doi.org/10.1016/j.drudis.2018.05.010] [PMID: 29750902]
[95]
Zhao L, Wang J, Pang L, Liu Y, Zhang J. GANsDTA: Predicting Drug-Target Binding Affinity Using GANs. Front Genet 2020; 10: 1243.
[http://dx.doi.org/10.3389/fgene.2019.01243] [PMID: 31993067]
[96]
Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol 2019; 16(7): 391-403.
[http://dx.doi.org/10.1038/s41585-019-0193-3] [PMID: 31092914]
[97]
Gertrudes JC, Maltarollo VG, Silva RA, Oliveira PR, Honório KM, da Silva ABF. Machine learning techniques and drug design. Curr Med Chem 2012; 19(25): 4289-97.
[http://dx.doi.org/10.2174/092986712802884259] [PMID: 22830342]
[98]
Chakravarti SK, Alla SRM. Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks. Frontiers in Artificial Intelligence 2019; 2: 17.
[http://dx.doi.org/10.3389/frai.2019.00017]
[99]
Liu HX, Zhang RS, Yao XJ, Liu MC, Hu ZD, Fan BT. QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: an inhibitor of AP-1 and NF-kappa B mediated gene expression based on support vector machines. J Chem Inf Comput Sci 2003; 43(4): 1288-96.
[http://dx.doi.org/10.1021/ci0340355] [PMID: 12870922]
[100]
Nekoei M, et al. QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach. Med Chem Res 2015; 24: 3037-46.
[http://dx.doi.org/10.1007/s00044-015-1354-4]
[101]
Wesley L, Veerapaneni S, Desai R, et al. 3D-QSAR and SVM Prediction of BRAF-V600E and HIV integrase inhibitors:A comparative study and characterization of performance with a new expected prediction perform ancemetric. Am J Biochem Biotechnol 2016; 12: 253-62.
[http://dx.doi.org/10.3844/ajbbsp.2016.253.262]
[102]
Garkani-Nejad Z, Ghanbari A. Application of support vector machine in QSAR study of triazolyl thiophenes as cyclin dependent kinase-5 inhibitors for their anti-alzheimer activity 2016.
[103]
Fatemi MH, Gharaghani S. A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine. Bioorg Med Chem 2007; 15(24): 7746-54.
[http://dx.doi.org/10.1016/j.bmc.2007.08.057] [PMID: 17870538]
[104]
Tang H, Wang XS, Huang XP, et al. Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation. J Chem Inf Model 2009; 49(2): 461-76.
[http://dx.doi.org/10.1021/ci800366f] [PMID: 19182860]
[105]
Ancuceanu R, Dinu M, Neaga I, Laszlo FG, Boda D. Development of QSAR machine learning-based models to forecast the effect of substances on malignant melanoma cells. Oncol Lett 2019; 17(5): 4188-96.
[http://dx.doi.org/10.3892/ol.2019.10068] [PMID: 31007759]
[106]
Douali L, Villemin D, Cherqaoui D. Neural networks: Accurate nonlinear QSAR model for HEPT derivatives. J Chem Inf Comput Sci 2003; 43(4): 1200-7.
[http://dx.doi.org/10.1021/ci034047q] [PMID: 12870912]
[107]
Basak SC, Nayarisseri A, González-Díaz H, Bonchev D. Editorial (Thematic Issue: Chemoinformatics Models for Pharmaceutical Design, Part 2). Curr Pharm Des 2016; 22(34): 5177-8.
[http://dx.doi.org/10.2174/138161282234161110222751] [PMID: 27852211]
[108]
Basak SC, Nayarisseri A, González-Díaz H, Bonchev D. Editorial (Thematic Issue: Chemoinformatics Models for Pharmaceutical Design, Part 1). Curr Pharm Des 2016; 22(33): 5041-2.
[http://dx.doi.org/10.2174/138161282233161109224932] [PMID: 27852204]
[109]
Kelotra A, Gokhale SM, Kelotra S, et al. Alkyloxy carbonyl modified hexapeptides as a high affinity compounds for Wnt5A protein in the treatment of psoriasis. Bioinformation 2014; 10(12): 743-9.
[http://dx.doi.org/10.6026/97320630010743] [PMID: 25670877]
[110]
Chandrakar B, Jain A, Roy S, et al. 2013.
[111]
Khandelwal R, Chauhan APS, Bilawat S, et al. 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): 2511-26.
[http://dx.doi.org/10.2174/1568026618666181115092001] [PMID: 30430945]
[112]
Nayarisseri A, Singh SK. Functional inhibition of VEGF and EGFR suppressors in cancer treatment. Curr Top Med Chem 2019; 19(3): 178-9.
[http://dx.doi.org/10.2174/156802661903190328155731] [PMID: 30950335]
[113]
Monteiro AFM, Viana JO, Nayarisseri A, et al. Computational Studies Applied to Flavonoids against Alzheimer’s and Parkinson’s Diseases. Oxid Med Cell Longev 2018; 20187912765
[http://dx.doi.org/10.1155/2018/7912765] [PMID: 30693065]
[114]
Patidar K, Panwar U, Vuree S, et al. 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): 1229-41.
[http://dx.doi.org/10.31557/APJCP.2019.20.4.1229] [PMID: 31030499]
[115]
Sharda S, Khandelwal R, Adhikary R, Sharma D, Majhi M, Hussain T, et al. 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(13): 1129-44.
[http://dx.doi.org/10.2174/1568026619666190521084941] [PMID: 31109278]
[116]
Fox T, Kriegl JM. Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem 2006; 6(15): 1579-91.
[http://dx.doi.org/10.2174/156802606778108915] [PMID: 16918470]
[117]
Hecht D. Applications of machine learning and computational intelligence to drug discovery and development. Drug Dev Res 2011; 72(1): 53-65.
[http://dx.doi.org/10.1002/ddr.20402]
[118]
Sudhakaran SL, Madathil D, Arumugam M, Sundararajan V. Drug development for hepatitis c virus infection: machine learning applicationsGlobal Virology III: Virology in the 21st Century. Cham: Springer 2019; pp. 117-29.
[http://dx.doi.org/10.1007/978-3-030-29022-1_6]
[119]
Ferreira LLG, Andricopulo AD. ADMET modeling approaches in drug discovery. Drug Discov Today 2019; 24(5): 1157-65.
[http://dx.doi.org/10.1016/j.drudis.2019.03.015] [PMID: 30890362]
[120]
Wang S, Sun H, Liu H, Li D, Li Y, Hou T. ADMET evaluation in drug discovery. 16. Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches. Mol Pharm 2016; 13(8): 2855-66.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00471] [PMID: 27379394]
[121]
Lei T, Sun H, Kang Y, et al. ADMET evaluation in drug discovery. 18. Reliable prediction of chemical-induced urinary tract toxicity by boosting machine learning approaches. Mol Pharm 2017; 14(11): 3935-53.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00631] [PMID: 29037046]
[122]
Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson AJ. Virtual screening with AutoDock: theory and practice. Expert Opin Drug Discov 2010; 5(6): 597-607.
[http://dx.doi.org/10.1517/17460441.2010.484460] [PMID: 21532931]
[123]
Lyne PD. Structure-based virtual screening: an overview. Drug Discov Today 2002; 7(20): 1047-55.
[http://dx.doi.org/10.1016/S1359-6446(02)02483-2] [PMID: 12546894]
[124]
Feinberg EN, Sheridan R, Joshi E, Pande VS, Cheng AC. 2019.
[125]
Lei T, Li Y, Song Y, Li D, Sun H, Hou T. ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J Cheminform 2016; 8(1): 6.
[http://dx.doi.org/10.1186/s13321-016-0117-7] [PMID: 26839598]
[126]
Montanari F, Kuhnke L, Ter Laak A, Clevert DA. Modeling physico-chemical admet endpoints with multitask graph convolutional networks. Molecules 2019; 25(1): 44.
[http://dx.doi.org/10.3390/molecules25010044] [PMID: 31877719]
[127]
Panteleev J, Gao H, Jia L. Recent applications of machine learning in medicinal chemistry. Bioorg Med Chem Lett 2018; 28(17): 2807-15.
[http://dx.doi.org/10.1016/j.bmcl.2018.06.046] [PMID: 30122222]
[128]
El Aissouq A, Toufik H, Stitou M, Ouammou A, Lamchouri F. In silico design of novel tetra-substituted pyridinylimidazoles derivatives as c-jun N-terminal kinase-3 inhibitors, using 2D/3D-QSAR studies, molecular docking and ADMET prediction. Int J Pept Res Ther 2020; 26(3): 1335-51.
[http://dx.doi.org/10.1007/s10989-019-09939-8]
[129]
Shoichet BK. Virtual screening of chemical libraries. Nature 2004; 432(7019): 862-5.
[http://dx.doi.org/10.1038/nature03197] [PMID: 15602552]
[130]
Guan L, Yang H, Cai Y, et al. ADMET-score - a comprehensive scoring function for evaluation of chemical drug-likeness. MedChemComm 2018; 10(1): 148-57.
[http://dx.doi.org/10.1039/C8MD00472B] [PMID: 30774861]
[131]
Han Y, Zhang J, Hu CQ, Zhang X, Ma B, Zhang P. In silico ADME and toxicity prediction of ceftazidime and its impurities. Front Pharmacol 2019; 10: 434.
[http://dx.doi.org/10.3389/fphar.2019.00434] [PMID: 31068821]
[132]
Zaki H, Belhassan A, Aouidate A, Lakhlifi T, Benlyas M, Bouachrine M. Antibacterial study of 3-(2-amino-6-phenylpyrimidin-4-yl)-N-cyclopropyl-1-methyl-1H-indole-2-carboxamide derivatives: CoMFA, CoMSIA analyses, molecular docking and ADMET properties prediction. J Mol Struct 2019; 1177: 275-85.
[http://dx.doi.org/10.1016/j.molstruc.2018.09.073]
[133]
Perkins AN, Inayat-Hussain SH, Deziel NC, et al. Evaluation of potential carcinogenicity of organic chemicals in synthetic turf crumb rubber. Environ Res 2019; 169: 163-72.
[http://dx.doi.org/10.1016/j.envres.2018.10.018] [PMID: 30458352]
[134]
Celik S, Albayrak AT, Akyuz S, Ozel AE. Synthesis, molecular docking and ADMET study of ionic liquid as anticancer inhibitors of DNA and COX-2, TOPII enzymes. J Biomol Struct Dyn 2019.
[PMID: 30955453]
[135]
Cai C, Guo P, Zhou Y, et al. Deep learning-based prediction of drug-induced cardiotoxicity. J Chem Inf Model 2019; 59(3): 1073-84.
[http://dx.doi.org/10.1021/acs.jcim.8b00769] [PMID: 30715873]
[136]
Uzzaman M, Shawon J, Siddique ZA. Molecular docking, dynamics simulation and ADMET prediction of Acetaminophen and its modified derivatives based on quantum calculations. SN Applied Sciences 2019; 1(11): 1437.
[http://dx.doi.org/10.1007/s42452-019-1442-z]
[137]
Mohammad T, Khan FI, Lobb KA, Islam A, Ahmad F, Hassan MI. Identification and evaluation of bioactive natural products as potential inhibitors of human microtubule affinity-regulating kinase 4 (MARK4). J Biomol Struct Dyn 2019; 37(7): 1813-29.
[http://dx.doi.org/10.1080/07391102.2018.1468282] [PMID: 29683402]
[138]
Melville JL, Burke EK, Hirst JD. Machine learning in virtual screening. Comb Chem High Throughput Screen 2009; 12(4): 332-43.
[http://dx.doi.org/10.2174/138620709788167980] [PMID: 19442063]
[139]
Bayrak N, Yıldırım H, Yıldız M, et al. Design, synthesis, and biological activity of Plastoquinone analogs as a new class of anticancer agents. Bioorg Chem 2019; 92103255
[http://dx.doi.org/10.1016/j.bioorg.2019.103255] [PMID: 31542717]
[140]
Gao Y, Wang H, Wang J, Cheng M. In silico studies on p21-activated kinase 4 inhibitors: comprehensive application of 3D-QSAR analysis, molecular docking, molecular dynamics simulations, and MM-GBSA calculation. J Biomol Struct Dyn 2020; 38(14): 4119-33.
[http://dx.doi.org/10.1080/07391102.2019.1673823] [PMID: 31556340]
[141]
Cong L, Dong X, Wang Y, Deng Y, Li B, Dai R. On the role of synthesized hydroxylated chalcones as dual functional amyloid-β aggregation and ferroptosis inhibitors for potential treatment of Alzheimer’s disease. Eur J Med Chem 2019; 166: 11-21.
[http://dx.doi.org/10.1016/j.ejmech.2019.01.039] [PMID: 30684867]
[142]
Sirous H, Chemi G, Campiani G, Brogi S. An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction. Comput Biol Chem 2019; 83107105
[http://dx.doi.org/10.1016/j.compbiolchem.2019.107105] [PMID: 31473433]
[143]
Vora J, Patel S, Sinha S, et al. Structure based virtual screening, 3D-QSAR, molecular dynamics and ADMET studies for selection of natural inhibitors against structural and non-structural targets of Chikungunya. J Biomol Struct Dyn 2019; 37(12): 3150-61.
[http://dx.doi.org/10.1080/07391102.2018.1509732] [PMID: 30114965]
[144]
Leite ACL, de Lima RS, Moreira DRDM, et al. Synthesis, docking, and in vitro activity of thiosemicarbazones, aminoacyl-thiosemicarbazides and acyl-thiazolidones against Trypanosoma cruzi. Bioorg Med Chem 2006; 14(11): 3749-57.
[http://dx.doi.org/10.1016/j.bmc.2006.01.034] [PMID: 16458521]
[145]
Rosati O, Curini M, Marcotullio MC, et al. Synthesis, docking studies and anti-inflammatory activity of 4,5,6,7-tetrahydro-2H-indazole derivatives. Bioorg Med Chem 2007; 15(10): 3463-73.
[http://dx.doi.org/10.1016/j.bmc.2007.03.006] [PMID: 17382550]
[146]
Vicik R, Busemann M, Gelhaus C, et al. Aziridide-based inhibitors of cathepsin L: synthesis, inhibition activity, and docking studies. ChemMedChem 2006; 1(10): 1126-41.
[http://dx.doi.org/10.1002/cmdc.200600106] [PMID: 16933358]
[147]
Irannejad H, Kebriaieezadeh A, Zarghi A, et al. Synthesis, docking simulation, biological evaluations and 3D-QSAR study of 5-Aryl-6-(4-methylsulfonyl)-3-(metylthio)-1,2,4-triazine as selective cyclooxygenase-2 inhibitors. Bioorg Med Chem 2014; 22(2): 865-73.
[http://dx.doi.org/10.1016/j.bmc.2013.12.002] [PMID: 24361187]
[148]
Sameem B, Saeedi M, Mahdavi M, et al. Synthesis, docking study and neuroprotective effects of some novel pyrano[3,2-c]chromene derivatives bearing morpholine/phenylpiperazine moiety. Bioorg Med Chem 2017; 25(15): 3980-8.
[http://dx.doi.org/10.1016/j.bmc.2017.05.043] [PMID: 28587871]
[149]
Ramajayam R, Tan KP, Liu HG, Liang PH. Synthesis, docking studies, and evaluation of pyrimidines as inhibitors of SARS-CoV 3CL protease. Bioorg Med Chem Lett 2010; 20(12): 3569-72.
[http://dx.doi.org/10.1016/j.bmcl.2010.04.118] [PMID: 20494577]
[150]
Kotaiah Y, Nagaraju K, Harikrishna N, Venkata Rao C, Yamini L, Vijjulatha M. Synthesis, docking and evaluation of antioxidant and antimicrobial activities of novel 1,2,4-triazolo[3,4-b][1,3,4]thiadiazol-6-yl)selenopheno[2,3-d]pyrimidines. Eur J Med Chem 2014; 75: 195-202.
[http://dx.doi.org/10.1016/j.ejmech.2014.01.006] [PMID: 24531232]
[151]
Nayab RS, Maddila S, Krishna MP, et al. In silico molecular docking and in vitro antioxidant activity studies of novel α-aminophosphonates bearing 6-amino-1,3-dimethyl uracil. J Recept Signal Transduct Res 2020; 40(2): 166-72.
[http://dx.doi.org/10.1080/10799893.2020.1722166] [PMID: 32019395]
[152]
Crestey F, Jensen AA, Soerensen C, et al. Dual Nicotinic Acetylcholine Receptor α4β2 Antagonists/α7 Agonists: Synthesis, Docking Studies, and Pharmacological Evaluation of Tetrahydroisoquinolines and Tetrahydroisoquinolinium Salts. J Med Chem 2018; 61(4): 1719-29.
[http://dx.doi.org/10.1021/acs.jmedchem.7b01895] [PMID: 29384668]
[153]
Amin KM, Georgey HH, Awadallah FM. EGFR tyrosine kinase targeted compounds: synthesis, docking study, and in vitro antitumor activity of some new quinazoline and benzo [d] isothiazole derivatives. Med Chem Res 2011; 20(7): 1042-53.
[http://dx.doi.org/10.1007/s00044-010-9437-8]
[154]
Naim MJ, Alam O, Alam MJ, Shaquiquzzaman M, Alam MM, Naidu VGM. Synthesis, docking, in vitro and in vivo antidiabetic activity of pyrazole-based 2,4-thiazolidinedione derivatives as PPAR-γ modulators. Arch Pharm (Weinheim) 2018; 351(3-4)e1700223
[http://dx.doi.org/10.1002/ardp.201700223] [PMID: 29400412]
[155]
Maccallini C, Montagnani M, Paciotti R, et al. Selective acetamidine-based nitric oxide synthase inhibitors: synthesis, docking, and biological studies. ACS Med Chem Lett 2015; 6(6): 635-40.
[http://dx.doi.org/10.1021/acsmedchemlett.5b00149] [PMID: 26101565]
[156]
Dandawate P, Khan E, Padhye S, et al. Synthesis, characterization, molecular docking and cytotoxic activity of novel plumbagin hydrazones against breast cancer cells. Bioorg Med Chem Lett 2012; 22(9): 3104-8.
[http://dx.doi.org/10.1016/j.bmcl.2012.03.060] [PMID: 22483392]
[157]
Bala V, Jangir S, Mandalapu D, et al. Dithiocarbamate-thiourea hybrids useful as vaginal microbicides also show reverse transcriptase inhibition: design, synthesis, docking and pharmacokinetic studies. Bioorg Med Chem Lett 2015; 25(4): 881-6.
[http://dx.doi.org/10.1016/j.bmcl.2014.12.062] [PMID: 25592712]
[158]
Bacharaju K, Jambula SR, Sivan S, Jyostnatangeda S, Manga V. Design, synthesis, molecular docking and biological evaluation of new dithiocarbamates substituted benzimidazole and chalcones as possible chemotherapeutic agents. Bioorg Med Chem Lett 2012; 22(9): 3274-7.
[http://dx.doi.org/10.1016/j.bmcl.2012.03.018] [PMID: 22460028]
[159]
Stingaci E, Zveaghinteva M, Pogrebnoi S, et al. New vinyl-1,2,4-triazole derivatives as antimicrobial agents: Synthesis, biological evaluation and molecular docking studies. Bioorg Med Chem Lett 2020; 30(17)127368
[http://dx.doi.org/10.1016/j.bmcl.2020.127368] [PMID: 32738986]
[160]
Khan KM, Rahim F, Wadood A, et al. Synthesis and molecular docking studies of potent α-glucosidase inhibitors based on biscoumarin skeleton. Eur J Med Chem 2014; 81: 245-52.
[http://dx.doi.org/10.1016/j.ejmech.2014.05.010] [PMID: 24844449]
[161]
Yerdelen KO, Tosun E. Synthesis, docking and biological evaluation of oxamide and fumaramide analogs as potential AChE and BuChE inhibitors. Med Chem Res 2015; 24(2): 588-602.
[http://dx.doi.org/10.1007/s00044-014-1152-4]
[162]
Atanasova M, Stavrakov G, Philipova I, Zheleva D, Yordanov N, Doytchinova I. Galantamine derivatives with indole moiety: Docking, design, synthesis and acetylcholinesterase inhibitory activity. Bioorg Med Chem 2015; 23(17): 5382-9.
[http://dx.doi.org/10.1016/j.bmc.2015.07.058] [PMID: 26260334]
[163]
Menteşe E, Bektaş H, Sokmen BB, Emirik M, Çakır D, Kahveci B. Synthesis and molecular docking study of some 5,6-dichloro-2-cyclopropyl-1H-benzimidazole derivatives bearing triazole, oxadiazole, and imine functionalities as potent inhibitors of urease. Bioorg Med Chem Lett 2017; 27(13): 3014-8.
[http://dx.doi.org/10.1016/j.bmcl.2017.05.019] [PMID: 28526368]
[164]
Zaib S, Saeed A, Stolte K, Flörke U, Shahid M, Iqbal J. New aminobenzenesulfonamide-thiourea conjugates: synthesis and carbonic anhydrase inhibition and docking studies. Eur J Med Chem 2014; 78: 140-50.
[http://dx.doi.org/10.1016/j.ejmech.2014.03.023] [PMID: 24681391]
[165]
Liu XH, Chen PQ, Wang BL, Li YH, Wang SH, Li ZM. Synthesis, bioactivity, theoretical and molecular docking study of 1-cyano-N-substituted-cyclopropanecarboxamide as ketol-acid reductoisomerase inhibitor. Bioorg Med Chem Lett 2007; 17(13): 3784-8.
[http://dx.doi.org/10.1016/j.bmcl.2007.04.003] [PMID: 17512731]
[166]
Zou Y, Zhao Q, Liao J, et al. New triazole derivatives as antifungal agents: synthesis via click reaction, in vitro evaluation and molecular docking studies. Bioorg Med Chem Lett 2012; 22(8): 2959-62.
[http://dx.doi.org/10.1016/j.bmcl.2012.02.042] [PMID: 22437114]
[167]
Gawali R, Trivedi J, Bhansali S, Bhosale R, Sarkar D, Mitra D. Design, synthesis, docking studies and biological screening of 2-thiazolyl substituted -2,3-dihydro-1H-naphtho[1,2-e][1,3]oxazines as potent HIV-1 reverse transcriptase inhibitors. Eur J Med Chem 2018; 157: 310-9.
[http://dx.doi.org/10.1016/j.ejmech.2018.07.067] [PMID: 30099253]
[168]
Abdelrazek FM, Gomha SM, Shaaban MEB, et al. One-pot three-component synthesis and molecular docking of some novel 2-thiazolyl pyridines as potent antimicrobial agents. Mini Rev Med Chem 2019; 19(6): 527-38.
[http://dx.doi.org/10.2174/1389557518666181019124104] [PMID: 30360710]
[169]
Ashraf Z, Rafiq M, Seo SY, Babar MM, Zaidi NU. Synthesis, kinetic mechanism and docking studies of vanillin derivatives as inhibitors of mushroom tyrosinase. Bioorg Med Chem 2015; 23(17): 5870-80.
[http://dx.doi.org/10.1016/j.bmc.2015.06.068] [PMID: 26204890]
[170]
Wang XD, Wei W, Wang PF, et al. Synthesis, molecular docking and biological evaluation of 3-arylfuran-2(5H)-ones as anti-gastric ulcer agent. Bioorg Med Chem 2015; 23(15): 4860-5.
[http://dx.doi.org/10.1016/j.bmc.2015.05.026] [PMID: 26048027]
[171]
Zhang XM, Qiu M, Sun J, et al. Synthesis, biological evaluation, and molecular docking studies of 1,3,4-oxadiazole derivatives possessing 1,4-benzodioxan moiety as potential anticancer agents. Bioorg Med Chem 2011; 19(21): 6518-24.
[http://dx.doi.org/10.1016/j.bmc.2011.08.013] [PMID: 21962523]
[172]
Sun J, Yang YS, Li W, et al. Synthesis, biological evaluation and molecular docking studies of 1,3,4-thiadiazole derivatives containing 1,4-benzodioxan as potential antitumor agents. Bioorg Med Chem Lett 2011; 21(20): 6116-21.
[http://dx.doi.org/10.1016/j.bmcl.2011.08.039] [PMID: 21889345]
[173]
Jasril J. New Fluorinated Chalcone and Pyrazolines Analogues: Synthesis, Docking and Molecular Dynamic Studies as Anticancer Agents. Thaiphesatchasan 2017; 41(3)
[174]
Hatti I, Sreenivasulu R, Jadav SS, Jayaprakash V, Kumar CG, Raju RR. Synthesis, cytotoxic activity and docking studies of new 4-aza-podophyllotoxin derivatives. Med Chem Res 2015; 24(8): 3305-13.
[http://dx.doi.org/10.1007/s00044-015-1375-z]
[175]
Desai V, Desai S, Gaonkar SN, Palyekar U, Joshi SD, Dixit SK. Novel quinoxalinyl chalcone hybrid scaffolds as enoyl ACP reductase inhibitors: Synthesis, molecular docking and biological evaluation. Bioorg Med Chem Lett 2017; 27(10): 2174-80.
[http://dx.doi.org/10.1016/j.bmcl.2017.03.059] [PMID: 28372908]
[176]
Makhaeva GF, Boltneva NP, Lushchekina SV, et al. Synthesis, molecular docking and biological evaluation of N,N-disubstituted 2-aminothiazolines as a new class of butyrylcholinesterase and carboxylesterase inhibitors. Bioorg Med Chem 2016; 24(5): 1050-62.
[http://dx.doi.org/10.1016/j.bmc.2016.01.031] [PMID: 26827140]
[177]
Altıntop MD, Sever B, Özdemir A, et al. Potential inhibitors of human carbonic anhydrase isozymes I and II: Design, synthesis and docking studies of new 1,3,4-thiadiazole derivatives. Bioorg Med Chem 2017; 25(13): 3547-54.
[http://dx.doi.org/10.1016/j.bmc.2017.05.005] [PMID: 28511907]
[178]
Mollica A, Costante R, Akdemir A, et al. Exploring new Probenecid-based carbonic anhydrase inhibitors: Synthesis, biological evaluation and docking studies. Bioorg Med Chem 2015; 23(17): 5311-8.
[http://dx.doi.org/10.1016/j.bmc.2015.07.066] [PMID: 26264840]
[179]
Swain SS, Paidesetty SK, Dehury B, et al. Molecular docking and simulation study for synthesis of alternative dapsone derivative as a newer antileprosy drug in multidrug therapy. J Cell Biochem 2018; 119(12): 9838-52.
[http://dx.doi.org/10.1002/jcb.27304] [PMID: 30125973]
[180]
Gautam R, Jachak SM, Kumar V, Mohan CG. Synthesis, biological evaluation and molecular docking studies of stellatin derivatives as cyclooxygenase (COX-1, COX-2) inhibitors and anti-inflammatory agents. Bioorg Med Chem Lett 2011; 21(6): 1612-6.
[http://dx.doi.org/10.1016/j.bmcl.2011.01.116] [PMID: 21345672]
[181]
Neelarapu R, Holzle DL, Velaparthi S, et al. Design, synthesis, docking, and biological evaluation of novel diazide-containing isoxazole- and pyrazole-based histone deacetylase probes. J Med Chem 2011; 54(13): 4350-64.
[http://dx.doi.org/10.1021/jm2001025] [PMID: 21548582]
[182]
Balupuri A, Lee DY, Lee MH, et al. Design, synthesis, docking and biological evaluation of 4-phenyl-thiazole derivatives as autotaxin (ATX) inhibitors. Bioorg Med Chem Lett 2017; 27(17): 4156-64.
[http://dx.doi.org/10.1016/j.bmcl.2017.07.022] [PMID: 28743508]
[183]
Wang G, Chen M, Wang J, et al. Synthesis, biological evaluation and molecular docking studies of chromone hydrazone derivatives as α-glucosidase inhibitors. Bioorg Med Chem Lett 2017; 27(13): 2957-61.
[http://dx.doi.org/10.1016/j.bmcl.2017.05.007] [PMID: 28506754]
[184]
Mohammadi-Khanaposhtani M, Saeedi M, Zafarghandi NS, et al. Potent acetylcholinesterase inhibitors: design, synthesis, biological evaluation, and docking study of acridone linked to 1,2,3-triazole derivatives. Eur J Med Chem 2015; 92: 799-806.
[http://dx.doi.org/10.1016/j.ejmech.2015.01.044] [PMID: 25636055]
[185]
Mendoza-Martínez C, Galindo-Sevilla N, Correa-Basurto J, et al. Antileishmanial activity of quinazoline derivatives: synthesis, docking screens, molecular dynamic simulations and electrochemical studies. Eur J Med Chem 2015; 92: 314-31.
[http://dx.doi.org/10.1016/j.ejmech.2014.12.051] [PMID: 25576738]

Rights & Permissions Print Cite
Article Metrics
85
1
© 2024 Bentham Science Publishers | Privacy Policy