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Review Article

机器学习技术在预测药物靶点结合亲和力中的应用:周期蛋白依赖性激酶2的研究

卷 28, 期 2, 2021

发表于: 02 November, 2019

页: [253 - 265] 页: 13

弟呕挨: 10.2174/2213275912666191102162959

价格: $65

摘要

背景：对周期蛋白依赖性激酶2 (CDK2)结构的阐明，使开发虚拟筛选的靶向评分功能成为可能，旨在识别该酶的新抑制剂。CDK2是一种用于开发调节细胞周期进展和控制药物的蛋白靶点。这类药物具有潜在的抗癌活性。对周期蛋白依赖性激酶2 (CDK2)结构的阐明，使开发虚拟筛选的靶向评分功能成为可能，旨在识别该酶的新抑制剂。CDK2是一种用于开发调节细胞周期进展和控制药物的蛋白靶点。这类药物具有潜在的抗癌活性。 目的：我们的目标是回顾机器学习方法在预测蛋白质靶点配体结合亲和力方面的最新应用。为了评估经典评分函数和目标评分函数的预测性能，我们集中分析了CDK2结构。 方法：我们有数百个CDK2与不同配体的二元配合物的实验结构数据，其中许多具有抑制常数信息。我们通过经典的评分函数和机器学习模型来研究计算CDK2结合亲和力的计算方法。 结果：对Molegro Virtual Docker、AutoDock4、Autodock Vina等对接程序中可用的经典评分函数的预测性能分析表明，这些方法未能预测出与实验数据具有显著相关性的绑定亲和力。通过监督机器学习技术开发的目标评分函数与实验数据显示显著相关。 结论：在这里，我们描述了有监督机器学习技术的应用，以生成一个评分函数来预测绑定亲和力。与经典评分函数相比，机器学习模型显示出了优越的预测性能。分析通过机器学习获得的计算模型可以捕捉到与CDK2结合亲和力的基本结构特征。

关键词: 机器学习，质点系统，CDK2，激酶，癌症，药物设计。

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[1]
Böhm, H.J. A novel computational tool for automated structure-based drug design. J. Mol. Recognit., 1993, 6(3), 131-137.
[http://dx.doi.org/10.1002/jmr.300060305] [PMID: 8060670]
[2]
Böhm, H.J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des., 1994, 8(3), 243-256.
[http://dx.doi.org/10.1007/BF00126743] [PMID: 7964925]
[3]
Böhm, H.J. Towards the automatic design of synthetically accessible protein ligands: peptides, amides and peptidomimetics. J. Comput. Aided Mol. Des., 1996, 10(4), 265-272.
[http://dx.doi.org/10.1007/BF00124496] [PMID: 8877698]
[4]
Stahl, M.; Böhm, H.J. Development of filter functions for protein-ligand docking. J. Mol. Graph. Model., 1998, 16(3), 121-132.
[http://dx.doi.org/10.1016/S1093-3263(98)00018-7] [PMID: 10434251]
[5]
Klebe, G.; Böhm, H.J. Energetic and entropic factors determining binding affinity in protein-ligand complexes. J. Recept. Signal Transduct. Res., 1997, 17(1-3), 459-473.
[http://dx.doi.org/10.3109/10799899709036621] [PMID: 9029508]
[6]
Böhm, H.J.; Banner, D.W.; Weber, L. Combinatorial docking and combinatorial chemistry: design of potent non-peptide thrombin inhibitors. J. Comput. Aided Mol. Des., 1999, 13(1), 51-56.
[http://dx.doi.org/10.1023/A:1008040531766] [PMID: 10087499]
[7]
De Azevedo, W.F. Jr. MolDock applied to structure-based virtual screening. Curr. Drug Targets, 2010, 11(3), 327-334.
[http://dx.doi.org/10.2174/138945010790711941] [PMID: 20210757]
[8]
Bleicher, K.H.; Böhm, H.J.; Müller, K.; Alanine, A.I. Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discov., 2003, 2(5), 369-378.
[http://dx.doi.org/10.1038/nrd1086] [PMID: 12750740]
[9]
Azevedo, L.S.; Moraes, F.P.; Xavier, M.M.; Pantoja, E.O.; Villavicencio, B.; Finck, J.A.; Proenca, A.M.; Rocha, K.B.; de Azevedo, W.F. Jr. Recent progress of molecular docking simulations applied to development of drugs. Curr. Bioinform., 2012, 7(4), 352-365.
[http://dx.doi.org/10.2174/157489312803901063]
[10]
Korb, O.; Stützle, T.; Exner, T.E. Empirical scoring functions for advanced protein-ligand docking with plants. J. Chem. Inf. Model., 2009, 49(1), 84-96.
[http://dx.doi.org/10.1021/ci800298z] [PMID: 19125657]
[11]
Khamis, M.A.; Gomaa, W.; Ahmed, W.F. Machine learning in computational docking. Artif. Intell. Med., 2015, 63(3), 135-152.
[http://dx.doi.org/10.1016/j.artmed.2015.02.002] [PMID: 25724101]
[12]
Wójcikowski, M.; Ballester, P.J.; Siedlecki, P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci. Rep., 2017, 7, 46710.
[http://dx.doi.org/10.1038/srep46710] [PMID: 28440302]
[13]
Fan, C.; Huang, Y. Identification of novel potential scaffold for class I HDACs inhibition: an in silico protocol based on virtual screening, molecular dynamics, mathematical analysis and machine learning. Biochem. Biophys. Res. Commun., 2017, 491(3), 800-806.
[http://dx.doi.org/10.1016/j.bbrc.2017.07.051] [PMID: 28705738]
[14]
Ericksen, S.S.; Wu, H.; Zhang, H.; Michael, L.A.; Newton, M.A.; Hoffmann, F.M.; Wildman, S.A. Machine learning consensus scoring improves performance across targets in structure-based virtual screening. J. Chem. Inf. Model., 2017, 57(7), 1579-1590.
[http://dx.doi.org/10.1021/acs.jcim.7b00153] [PMID: 28654262]
[15]
Li, Y.; Yang, J. Structural and sequence similarity makes a significant impact on machine-learning-based scoring functions for protein-ligand interactions. J. Chem. Inf. Model., 2017, 57(4), 1007-1012.
[http://dx.doi.org/10.1021/acs.jcim.7b00049] [PMID: 28358210]
[16]
Wang, C.; Zhang, Y. Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J. Comput. Chem., 2017, 38(3), 169-177.
[http://dx.doi.org/10.1002/jcc.24667] [PMID: 27859414]
[17]
Heck, G.S.; Pintro, V.O.; Pereira, R.R.; de Ávila, M.B.; Levin, N.M.B.; de Azevedo, W.F. Supervised machine learning methods applied to predict ligand-binding affinity. Curr. Med. Chem., 2017, 24(23), 2459-2470.
[http://dx.doi.org/10.2174/0929867324666170623092503] [PMID: 28641555]
[18]
Smith, J.M. Natural selection and the concept of a protein space. Nature, 1970, 225(5232), 563-564.
[http://dx.doi.org/10.1038/225563a0] [PMID: 5411867]
[19]
Bohacek, R.S.; McMartin, C.; Guida, W.C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev., 1996, 16(1), 3-50.
[http://dx.doi.org/10.1002/(SICI)1098-1128(199601)16:1<3:AID-MED1>3.0.CO;2-6] [PMID: 8788213]
[20]
Dobson, C.M. Chemical space and biology. Nature, 2004, 432(7019), 824-828.
[http://dx.doi.org/10.1038/nature03192] [PMID: 15602547]
[21]
Kirkpatrick, P.; Ellis, C. Chemical space. Nature, 2004, 432(7019), 823.
[http://dx.doi.org/10.1038/432823a]
[22]
Lipinski, C.; Hopkins, A. Navigating chemical space for biology and medicine. Nature, 2004, 432(7019), 855-861.
[http://dx.doi.org/10.1038/nature03193] [PMID: 15602551]
[23]
Shoichet, B.K. Virtual screening of chemical libraries. Nature, 2004, 432(7019), 862-865.
[http://dx.doi.org/10.1038/nature03197] [PMID: 15602552]
[24]
Stockwell, B.R. Exploring biology with small organic molecules. Nature, 2004, 432(7019), 846-854.
[http://dx.doi.org/10.1038/nature03196] [PMID: 15602550]
[25]
Thomsen, R.; Christensen, M.H. MolDock: a new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.
[http://dx.doi.org/10.1021/jm051197e] [PMID: 16722650]
[26]
Heberlé, G.; de Azevedo, W.F. Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr. Med. Chem., 2011, 18(9), 1339-1352.
[http://dx.doi.org/10.2174/092986711795029573] [PMID: 21366530]
[27]
Goodsell, D.S.; Olson, A.J. Automated docking of substrates to proteins by simulated annealing. Proteins, 1990, 8(3), 195-202.
[http://dx.doi.org/10.1002/prot.340080302] [PMID: 2281083]
[28]
Morris, G.M.; Goodsell, D.S.; Huey, R.; Olson, A.J. Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. J. Comput. Aided Mol. Des., 1996, 10(4), 293-304.
[http://dx.doi.org/10.1007/BF00124499] [PMID: 8877701]
[29]
Morris, G.M.; Goodsell, D.S.; Halliday, R.S.; Huey, R.; Hart, W.E.; Belew, R.K.; Olson, A.J. Automated docking using a lamarckian genetic algorithm and empirical binding free energy function. J. Comput. Chem., 1998, 19(14), 1639-1662.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19981115)19: 14<1639:AID-JCC10>3.0.CO;2-B]
[30]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[31]
Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[32]
Xavier, M.M.; Heck, G.S.; Avila, M.B.; Levin, N.M.B.; Pintro, V.O.; Carvalho, N.L.; Azevedo, W.F. Jr. SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb. Chem. High Throughput Screen., 2016, 19(10), 801-812.
[http://dx.doi.org/10.2174/1386207319666160927111347] [PMID: 27686428]
[33]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[34]
Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 2007, 35(Database issue), D198-D201.
[http://dx.doi.org/10.1093/nar/gkl999] [PMID: 17145705]
[35]
Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053.
[http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]
[36]
Benson, M.L.; Smith, R.D.; Khazanov, N.A.; Dimcheff, B.; Beaver, J.; Dresslar, P.; Nerothin, J.; Carlson, H.A. Binding MOAD, a high-quality protein-ligand database. Nucleic Acids Res., 2008, 36(Database issue), D674-D678.
[http://dx.doi.org/10.1093/nar/gkm911] [PMID: 18055497]
[37]
Ahmed, A.; Smith, R.D.; Clark, J.J.; Dunbar, J.B. Jr; Carlson, H.A. Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures. Nucleic Acids Res., 2015, 43(Database issue), D465-D469.
[http://dx.doi.org/10.1093/nar/gku1088] [PMID: 25378330]
[38]
Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem., 2004, 47(12), 2977-2980.
[http://dx.doi.org/10.1021/jm030580l] [PMID: 15163179]
[39]
Liu, Z.; Li, Y.; Han, L.; Li, J.; Liu, J.; Zhao, Z.; Nie, W.; Liu, Y.; Wang, R. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics, 2015, 31(3), 405-412.
[http://dx.doi.org/10.1093/bioinformatics/btu626] [PMID: 25301850]
[40]
Tai, H.K.; Jusoh, S.A.; Siu, S.W.I. Chaos-embedded particle swarm optimization approach for protein-ligand docking and virtual screening. J. Cheminform., 2018, 10(1), 62.
[http://dx.doi.org/10.1186/s13321-018-0320-9] [PMID: 30552524]
[41]
Zhenin, M.; Bahia, M.S.; Marcou, G.; Varnek, A.; Senderowitz, H.; Horvath, D. Rescoring of docking poses under Occam’s Razor: are there simpler solutions? J. Comput. Aided Mol. Des., 2018, 32(9), 877-888.
[http://dx.doi.org/10.1007/s10822-018-0155-5] [PMID: 30173397]
[42]
Sunseri, J.; King, J.E.; Francoeur, P.G.; Koes, D.R. Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J. Comput. Aided Mol. Des., 2019, 33(1), 19-34.
[http://dx.doi.org/10.1007/s10822-018-0133-y] [PMID: 29992528]
[43]
Gaillard, T. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 benchmark. J. Chem. Inf. Model., 2018, 58(8), 1697-1706.
[http://dx.doi.org/10.1021/acs.jcim.8b00312] [PMID: 29989806]
[44]
Ramírez, D.; Caballero, J. Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules, 2018, 23(5)E1038
[http://dx.doi.org/10.3390/molecules23051038] [PMID: 29710787]
[45]
Shamsara, J. Correlation between virtual screening performance and binding site descriptors of protein targets. Int. J. Med. Chem., 2018, 2018(11)3829307
[http://dx.doi.org/10.1155/2018/3829307] [PMID: 29545955]
[46]
Zhang, L.; Ai, H.X.; Li, S.M.; Qi, M.Y.; Zhao, J.; Zhao, Q.; Liu, H.S. Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function. Oncotarget, 2017, 8(47), 83142-83154.
[http://dx.doi.org/10.18632/oncotarget.20915] [PMID: 29137330]
[47]
Saxena, A.; Mishra, S. Marine sponge derived natural products as inhibitors of mycothiol-S-conjugate amidase. Bioinformation, 2017, 13(8), 256-260.
[http://dx.doi.org/10.6026/97320630013256] [PMID: 28959094]
[48]
Kadukova, M.; Grudinin, S. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2. J. Comput. Aided Mol. Des., 2018, 32(1), 151-162.
[http://dx.doi.org/10.1007/s10822-017-0062-1] [PMID: 28913782]
[49]
Guan, B.; Zhang, C.; Ning, J. Genetic algorithm with a crossover elitist preservation mechanism for protein-ligand docking. AMB Express, 2017, 7(1), 174.
[http://dx.doi.org/10.1186/s13568-017-0476-0] [PMID: 28905320]
[50]
Selwa, E.; Elisée, E.; Zavala, A.; Iorga, B.I. Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations. J. Comput. Aided Mol. Des., 2018, 32(1), 273-286.
[http://dx.doi.org/10.1007/s10822-017-0054-1] [PMID: 28865056]
[51]
Ragoza, M.; Hochuli, J.; Idrobo, E.; Sunseri, J.; Koes, D.R. Protein-ligand scoring with convolutional neural networks. J. Chem. Inf. Model., 2017, 57(4), 942-957.
[http://dx.doi.org/10.1021/acs.jcim.6b00740] [PMID: 28368587]
[52]
Gangopadhyay, A.; Chakraborty, H.J.; Datta, A. Targeting the dengue β-OG with serotype-specific alkaloid virtual leads. J. Mol. Graph. Model., 2017, 73, 129-142.
[http://dx.doi.org/10.1016/j.jmgm.2017.02.018] [PMID: 28279821]
[53]
Li, H.; Leung, K.S.; Wong, M.H.; Ballester, P.J. Correcting the impact of docking pose generation error on binding affinity prediction. BMC Bioinformatics, 2016, 17(Suppl. 11), 308.
[http://dx.doi.org/10.1186/s12859-016-1169-4] [PMID: 28185549]
[54]
Koebel, M.R.; Cooper, A.; Schmadeke, G.; Jeon, S.; Narayan, M.; Sirimulla, S. S···O and S···N sulfur bonding interactions in protein-ligand complexes: empirical considerations and scoring function. J. Chem. Inf. Model., 2016, 56(12), 2298-2309.
[http://dx.doi.org/10.1021/acs.jcim.6b00236] [PMID: 27936771]
[55]
Uehara, S.; Tanaka, S. AutoDock-GIST: incorporating thermodynamics of active-site water into scoring function for accurate protein-ligand docking. Molecules, 2016, 21(11)E1604
[http://dx.doi.org/10.3390/molecules21111604] [PMID: 27886114]
[56]
Grudinin, S.; Kadukova, M.; Eisenbarth, A.; Marillet, S.; Cazals, F. Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R grand challenge using a physical model with a statistical parameter estimation. J. Comput. Aided Mol. Des., 2016, 30(9), 791-804.
[http://dx.doi.org/10.1007/s10822-016-9976-2] [PMID: 27718029]
[57]
Selwa, E.; Martiny, V.Y.; Iorga, B.I. Molecular docking performance evaluated on the D3R grand challenge 2015 drug-like ligand datasets. J. Comput. Aided Mol. Des., 2016, 30(9), 829-839.
[http://dx.doi.org/10.1007/s10822-016-9983-3] [PMID: 27699554]
[58]
Koebel, M.R.; Schmadeke, G.; Posner, R.G.; Sirimulla, S. AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina. J. Cheminform., 2016, 8, 27.
[http://dx.doi.org/10.1186/s13321-016-0139-1] [PMID: 27195023]
[59]
Anand, R. Identification of potential antituberculosis drugs through docking and virtual screening. Interdiscip. Sci., 2018, 10(2), 419-429.
[http://dx.doi.org/10.1007/s12539-016-0175-6] [PMID: 27147082]
[60]
Nivedha, A.K.; Thieker, D.F.; Makeneni, S.; Hu, H.; Woods, R.J. Vina-Carb: improving glycosidic angles during carbohydrate docking. J. Chem. Theory Comput., 2016, 12(2), 892-901.
[http://dx.doi.org/10.1021/acs.jctc.5b00834] [PMID: 26744922]
[61]
Zhu, X.; Shin, W.H.; Kim, H.; Kihara, D. Combined approach of patch-surfer and pl-patchsurfer for protein-ligand binding prediction in CSAR 2013 and 2014. J. Chem. Inf. Model., 2016, 56(6), 1088-1099.
[http://dx.doi.org/10.1021/acs.jcim.5b00625] [PMID: 26691286]
[62]
Pradeep, P.; Struble, C.; Neumann, T.; Sem, D.S.; Merrill, S.J. A novel scoring based distributed protein docking application to improve enrichment. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2015, 12(6), 1464-1469.
[http://dx.doi.org/10.1109/TCBB.2015.2401020] [PMID: 26671816]
[63]
Tanchuk, V.Y.; Tanin, V.O.; Vovk, A.I.; Poda, G. A new, improved hybrid scoring function for molecular docking and scoring based on AutoDock and AutoDock Vina. Chem. Biol. Drug Des., 2016, 87(4), 618-625.
[http://dx.doi.org/10.1111/cbdd.12697] [PMID: 26643167]
[64]
Ravindranath, P.A.; Forli, S.; Goodsell, D.S.; Olson, A.J.; Sanner, M.F. AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility. PLOS Comput. Biol., 2015, 11(12)e1004586
[http://dx.doi.org/10.1371/journal.pcbi.1004586] [PMID: 26629955]
[65]
Hidayat, A.N.; Aki-Yalcin, E.; Beksac, M.; Tian, E.; Usmani, S.Z.; Ertan-Bolelli, T.; Yalcin, I. Insight into human protease activated receptor-1 as anticancer target by molecular modelling. SAR QSAR Environ. Res., 2015, 26(10), 795-807.
[http://dx.doi.org/10.1080/1062936X.2015.1095799] [PMID: 26501801]
[66]
Nanard, M.; Nanard, J. A user-friendly biological workstation. Biochimie, 1985, 67(5), 429-432.
[http://dx.doi.org/10.1016/S0300-9084(85)80259-5] [PMID: 3839688]
[67]
Hirst, J.D.; King, R.D.; Sternberg, M.J. Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines. J. Comput. Aided Mol. Des., 1994, 8(4), 405-420.
[http://dx.doi.org/10.1007/BF00125375] [PMID: 7815092]
[68]
Hirst, J.D.; King, R.D.; Sternberg, M.J. Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines. J. Comput. Aided Mol. Des., 1994, 8(4), 421-432.
[http://dx.doi.org/10.1007/BF00125376] [PMID: 7815093]
[69]
de Azevedo, W.F. Jr. Opinion paper: targeting multiple cyclin-dependent kinases (CDKs): a new strategy for molecular docking studies. Curr. Drug Targets, 2016, 17(1), 2.
[http://dx.doi.org/10.2174/138945011701151217100907] [PMID: 26687602]
[70]
Ain, Q.U.; Aleksandrova, A.; Roessler, F.D.; Ballester, P.J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2015, 5(6), 405-424.
[http://dx.doi.org/10.1002/wcms.1225] [PMID: 27110292]
[71]
Xue, L.C.; Dobbs, D.; Bonvin, A.M.; Honavar, V. Computational prediction of protein interfaces: a review of data driven methods. FEBS Lett., 2015, 589(23), 3516-3526.
[http://dx.doi.org/10.1016/j.febslet.2015.10.003] [PMID: 26460190]
[72]
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Verplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E. Scikit-learn: machine learning in python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
[73]
Li, H.; Peng, J.; Leung, Y.; Leung, K.S.; Wong, M.H.; Lu, G.; Ballester, P.J. The impact of protein structure and sequence similarity on the accuracy of machine-learning scoring functions for binding affinity prediction. Biomolecules, 2018, 8(1), 12.
[http://dx.doi.org/10.3390/biom8010012] [PMID: 29538331]
[74]
Bitencourt-Ferreira, G.; de Azevedo, W.F. Jr. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys. Chem., 2018, 240, 63-69.
[http://dx.doi.org/10.1016/j.bpc.2018.05.010] [PMID: 29906639]
[75]
Jiménez, J.; Škalič, M.; Martínez-Rosell, G.; De Fabritiis, G. KDEEP: protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J. Chem. Inf. Model., 2018, 58(2), 287-296.
[http://dx.doi.org/10.1021/acs.jcim.7b00650] [PMID: 29309725]
[76]
de Ávila, M.B.; de Azevedo, W.F. Jr. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem. Biol. Drug Des., 2018, 92(2), 1468-1474.
[http://dx.doi.org/10.1111/cbdd.13312] [PMID: 29676519]
[77]
Amaral, M.E.A.; Nery, L.R.; Leite, C.E.; de Azevedo, W.F. Jr.; Campos, M.M. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest. New Drugs, 2018, 36(5), 782-796.
[http://dx.doi.org/10.1007/s10637-018-0568-y] [PMID: 29392539]
[78]
Freitas, P.G.; Elias, T.C.; Pinto, I.A.; Costa, L.T.; de Carvalho, P.V.S.D.; Omote, D.Q.; Camps, I.; Ishikawa, T.; Arcuri, H.A.; Vinga, S.; Oliveira, A.L.; Azevedo, W.F. Jr.; da Silveira, N.J.F. Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein. Lett. Drug Des. Discov., 2018, 15(5), 488-499.
[http://dx.doi.org/10.2174/1570180814666170810120150]
[79]
Hochuli, J.; Helbling, A.; Skaist, T.; Ragoza, M.; Koes, D.R. Visualizing convolutional neural network protein-ligand scoring. J. Mol. Graph. Model., 2018, 84, 96-108.
[http://dx.doi.org/10.1016/j.jmgm.2018.06.005] [PMID: 29940506]
[80]
Afifi, K.; Al-Sadek, A.F. Improving classical scoring functions using random forest: the non-additivity of free energy terms’ contributions in binding. Chem. Biol. Drug Des., 2018, 92(2), 1429-1434.
[http://dx.doi.org/10.1111/cbdd.13206] [PMID: 29655201]
[81]
Li, H.; Leung, K.S.; Wong, M.H.; Ballester, P.J. Low-quality structural and interaction data improves binding affinity prediction via random forest. Molecules, 2015, 20(6), 10947-10962.
[http://dx.doi.org/10.3390/molecules200610947] [PMID: 26076113]
[82]
Li, H.; Leung, K.S.; Wong, M.H.; Ballester, P.J. Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol. Inform., 2015, 34(2-3), 115-126.
[http://dx.doi.org/10.1002/minf.201400132] [PMID: 27490034]
[83]
Zilian, D.; Sotriffer, C.A. SFCscore(RF): a random forest-based scoring function for improved affinity prediction of protein-ligand complexes. J. Chem. Inf. Model., 2013, 53(8), 1923-1933.
[http://dx.doi.org/10.1021/ci400120b] [PMID: 23705795]
[84]
Ballester, P.J.; Mitchell, J.B. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics, 2010, 26(9), 1169-1175.
[http://dx.doi.org/10.1093/bioinformatics/btq112] [PMID: 20236947]
[85]
Kot, M.; Nagahashi, H.; Szymczak, P. Elastic moduli of simple mass spring models. Vis. Comput., 2015, 31(10), 1339-1350.
[http://dx.doi.org/10.1007/s00371-014-1015-5]
[86]
Kim, M.H.; Kim, D.; Choi, J.B.; Kim, M.K. Vibrational characteristics of graphene sheets elucidated using an elastic network model. Phys. Chem. Chem. Phys., 2014, 16(29), 15263-15271.
[http://dx.doi.org/10.1039/c4cp00732h] [PMID: 24939373]
[87]
Zhan, M.; Liu, S.; He, Z. Matching rules for collective behaviors on complex networks: optimal configurations for vibration frequencies of networked harmonic oscillators. PLoS One, 2013, 8(12)e82161
[http://dx.doi.org/10.1371/journal.pone.0082161] [PMID: 24386088]
[88]
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol., 1996, 58(1), 267-288.
[http://dx.doi.org/10.1111/j.2517-6161.1996.tb02080.x]
[89]
Tikhonov, A.N. On the regularization of ill-posed problems. Dokl. Akad. Nauk SSSR, 1963, 153, 49-52.
[90]
Zou, H.; Hastie, T. Addendum: Regularization and variable selection via the elastic net. Royal Stat. Soc., 2005, 67(2), 301-220.
[http://dx.doi.org/10.1111/j.1467-9868.2005.00503.x]
[91]
Legendre, A.M. Nouvelle méthodes pour la déterminiation des orbites des comètes; F; Didot, 1805.
[92]
Zar, J.H. Significance testing of the spearman rank correlation coefficient. J. Am. Stat. Assoc., 1972, 67(339), 578-580.
[http://dx.doi.org/10.1080/01621459.1972.10481251]
[93]
Levin, N.M.B.; Pintro, V.O.; Bitencourt-Ferreira, G.; de Mattos, B.B.; de Castro Silvério, A.; de Azevedo, W.F. Jr. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys. Chem., 2018, 235, 1-8.
[http://dx.doi.org/10.1016/j.bpc.2018.01.004] [PMID: 29407904]
[94]
de Ávila, M.B.; Xavier, M.M.; Pintro, V.O.; de Azevedo, W.F. Jr. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem. Biophys. Res. Commun., 2017, 494(1-2), 305-310.
[http://dx.doi.org/10.1016/j.bbrc.2017.10.035] [PMID: 29017921]
[95]
De Azevedo, W.F. Jr.; Mueller-Dieckmann, H.J.; Schulze-Gahmen, U.; Worland, P.J.; Sausville, E.; Kim, S.H. Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc. Natl. Acad. Sci. USA, 1996, 93(7), 2735-2740.
[http://dx.doi.org/10.1073/pnas.93.7.2735] [PMID: 8610110]
[96]
Kim, S.H.; Schulze-Gahmen, U.; Brandsen, J.; de Azevedo, W.F. Jr. Structural basis for chemical inhibition of CDK2. Prog. Cell Cycle Res., 1996, 2, 137-145.
[http://dx.doi.org/10.1007/978-1-4615-5873-6_14] [PMID: 9552391]
[97]
De Azevedo, W.F.; Leclerc, S.; Meijer, L.; Havlicek, L.; Strnad, M.; Kim, S.H. Inhibition of cyclin-dependent kinases by purine analogues: crystal structure of human cdk2 complexed with roscovitine. Eur. J. Biochem., 1997, 243(1-2), 518-526.
[http://dx.doi.org/10.1111/j.1432-1033.1997.0518a.x] [PMID: 9030780]
[98]
de Azevedo, W.F. Jr; Canduri, F.; da Silveira, N.J. Structural basis for inhibition of cyclin-dependent kinase 9 by flavopiridol. Biochem. Biophys. Res. Commun., 2002, 293(1), 566-571.
[http://dx.doi.org/10.1016/S0006-291X(02)00266-8] [PMID: 12054639]
[99]
de Azevedo, W.F. Jr.; Gaspar, R.T.; Canduri, F.; Camera, J.C.Jr.; da Silveira, N.J.F. Molecular model of cyclin-dependent kinase 5 complexed with roscovitine. Biochem. Biophys. Res. Commun., 2002, 297(5), 1154-1158.
[http://dx.doi.org/10.1016/S0006-291X(02)02352-5] [PMID: 12372407]
[100]
Canduri, F.; Uchoa, H.B.; de Azevedo, W.F. Jr. Molecular models of cyclin-dependent kinase 1 complexed with inhibitors. Biochem. Biophys. Res. Commun., 2004, 324(2), 661-666.
[http://dx.doi.org/10.1016/j.bbrc.2004.09.109] [PMID: 15474478]
[101]
Canduri, F.; de Azevedo, W.F. Jr. Structural basis for interaction of inhibitors with cyclin-dependent kinase 2. Curr. Comput. Aided. Drug. Des., 2005, 1(1), 53-64.
[http://dx.doi.org/10.2174/1573409052952233]
[102]
Manhani, K.K.; Arcuri, H.A.; da Silveira, N.J.; Uchôa, H.B.; de Azevedo, W.F. Jr.; Canduri, F. Molecular models of protein kinase 6 from Plasmodium falciparum. J. Mol. Model., 2005, 12(1), 42-48.
[http://dx.doi.org/10.1007/s00894-005-0002-1] [PMID: 16096806]
[103]
Leopoldino, A.M.; Canduri, F.; Cabral, H.; Junqueira, M.; de Marqui, A.B.; Apponi, L.H.; da Fonseca, I.O.; Domont, G.B.; Santos, D.S.; Valentini, S.; Bonilla-Rodriguez, G.O.; Fossey, M.A.; de Azevedo, W.F. Jr.; Tajara, E.H. Expression, purification, and circular dichroism analysis of human CDK9. Protein Expr. Purif., 2006, 47(2), 614-620.
[http://dx.doi.org/10.1016/j.pep.2006.02.012] [PMID: 16580843]
[104]
Krystof, V.; Cankar, P.; Frysová, I.; Slouka, J.; Kontopidis, G.; Dzubák, P.; Hajdúch, M.; Srovnal, J.; de Azevedo, W.F., Jr; Orság, M.; Paprskárová, M.; Rolcík, J.; Látr, A.; Fischer, P.M.; Strnad, M. 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity, and cellular effects. J. Med. Chem., 2006, 49(22), 6500-6509.
[http://dx.doi.org/10.1021/jm0605740] [PMID: 17064068]
[105]
Canduri, F.; Perez, P.C.; Caceres, R.A.; de Azevedo, W.F. Jr. CDK9 a potential target for drug development. Med. Chem., 2008, 4(3), 210-218.
[http://dx.doi.org/10.2174/157340608784325205] [PMID: 18473913]
[106]
Perez, P.C.; Caceres, R.A.; Canduri, F.; de Azevedo, W.F. Jr. Molecular modeling and dynamics simulation of human cyclin-dependent kinase 3 complexed with inhibitors. Comput. Biol. Med., 2009, 39(2), 130-140.
[http://dx.doi.org/10.1016/j.compbiomed.2008.11.004] [PMID: 19152876]
[107]
Levin, N.M.B.; Pintro, V.O.; de Ávila, M.B.; de Mattos, B.B.; De Azevedo, W.F. Jr. Understanding the structural basis for inhibition of cyclin-dependent kinases. New pieces in the molecular puzzle. Curr. Drug Targets, 2017, 18(9), 1104-1111.
[http://dx.doi.org/10.2174/1389450118666161116130155] [PMID: 27848884]
[108]
Volkart, P.A.; Bitencourt-Ferreira, G.; Souto, A.A.; de Azevedo, W.F. Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr. Drug Targets, 2019, 20(7), 716-726.
[http://dx.doi.org/10.2174/1389450120666181204165344] [PMID: 30516105]
[109]
Coracini, J.D.; de Azevedo, W.F. Jr. Shikimate kinase, a protein target for drug design. Curr. Med. Chem., 2014, 21(5), 592-604.
[http://dx.doi.org/10.2174/09298673113206660299] [PMID: 24164195]
[110]
Fujino, A.; Fukushima, K.; Kubota, T.; Kosugi, T.; Takimoto-Kamimura, M. Crystal structure of human cyclin-dependent kinase-2 complex with MK2 inhibitor TEI-I01800: insight into the selectivity. J. Synchrotron Radiat., 2013, 20(Pt 6), 905-909.
[http://dx.doi.org/10.1107/S0909049513020736] [PMID: 24121337]
[111]
Seifert, M.H. Targeted scoring functions for virtual screening. Drug Discov. Today, 2009, 14(11-12), 562-569.
[http://dx.doi.org/10.1016/j.drudis.2009.03.013] [PMID: 19508918]
[112]
Pintro, V.O.; de Azevedo, W.F. Jr. Optimized virtual screening workflow: towards target-based polynomial scoring functions for HIV-1 protease. Comb. Chem. High Throughput Screen., 2017, 20(9), 820-827.
[http://dx.doi.org/10.2174/1386207320666171121110019] [PMID: 29165067]
[113]
Chen, L.; Calin, G.A.; Zhang, S. Novel insights of structure-based modeling for RNA-targeted drug discovery. J. Chem. Inf. Model., 2012, 52(10), 2741-2753.
[http://dx.doi.org/10.1021/ci300320t] [PMID: 22947071]
[114]
Li, H.; Peng, J.; Sidorov, P.; Leung, Y.; Leung, K.S.; Wong, M.H.; Lu, G.; Ballester, P.J. Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinformatics, 2019, 35(20), 3989-3995.
[http://dx.doi.org/10.1093/bioinformatics/btz183] [PMID: 30873528]
[115]
Yasuo, N.; Sekijima, M. Improved method of structure-based virtual screening via interaction-energy-based learning. J. Chem. Inf. Model., 2019, 59(3), 1050-1061.
[http://dx.doi.org/10.1021/acs.jcim.8b00673] [PMID: 30808172]
[116]
Nogueira, M.S.; Koch, O. The development of target-specific machine learning models as scoring functions for docking-based target prediction. J. Chem. Inf. Model., 2019, 59(3), 1238-1252.
[http://dx.doi.org/10.1021/acs.jcim.8b00773] [PMID: 30802041]
[117]
Guedes, I.A.; Pereira, F.S.S.; Dardenne, L.E. Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front. Pharmacol., 2018, 9, 1089.
[http://dx.doi.org/10.3389/fphar.2018.01089] [PMID: 30319422]
[118]
Stepniewska-Dziubinska, M.M.; Zielenkiewicz, P.; Siedlecki, P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics, 2018, 34(21), 3666-3674.
[http://dx.doi.org/10.1093/bioinformatics/bty374] [PMID: 29757353]
[119]
Ashtawy, H.M.; Mahapatra, N.R. Boosted neural networks scoring functions for accurate ligand docking and ranking. J. Bioinform. Comput. Biol., 2018, 16(2)1850004
[http://dx.doi.org/10.1142/S021972001850004X] [PMID: 29495922]
[120]
Silva, A.D.; Bitencourt-Ferreira, G.; de Azevedo, W.F. Jr. Taba: a tool to analyze the binding affinity. J. Comput. Chem. 2020, 41(1), 69-73.
[http://dx.doi.org/10.1002/JCC.26048] [PMID: 31410856]

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