References
- Abad-Zapatero, C., & Metz, J. T. (2005). Ligand efficiency indices as guideposts for drug discovery. Drug Discovery Today, 10(7), 464–469. https://doi.org/10.1016/S1359-6446(05)03386-6
- Alavijeh, M. S., Chishty, M., Qaiser, M. Z., & Palmer, A. M. (2005). Drug metabolism and pharmacokinetics, the blood-brain barrier, and central nervous system drug discovery. NeuroRX, 2(4), 554–571. https://doi.org/10.1602/neurorx.2.4.554
- Alexander, L. T., Möbitz, H., Drueckes, P., Savitsky, P., Fedorov, O., Elkins, J. M., Deane, C. M., Cowan-Jacob, S. W., & Knapp, S. (2015). Type II inhibitors targeting CDK2. ACS Chemical Biology, 10(9), 2116–2125. https://doi.org/10.1021/acschembio.5b00398
- Anderson, A. C., & Wright, D. L. (2005). The design and docking of virtual compound libraries to structures of drug targets. Current Computer Aided-Drug Design, 1(1), 103–127. https://doi.org/10.2174/1573409052952279
- Apsel, B., Blair, J. A., Gonzalez, B., Nazif, T. M., Feldman, M. E., Aizenstein, B., Hoffman, R., Williams, R. L., Shokat, K. M., & Knight, Z. A. (2008). Targeted polypharmacology: Discovery of dual inhibitors of tyrosine and phosphoinositide kinases. Nature Chemical Biology, 4(11), 691–699. https://doi.org/10.1038/nchembio.117
- Bach, F. R., Heckerman, D., & Horvitz, E. (2005). On the Path to an Ideal ROC Curve: Considering Cost Asymmetry in Learning Classifiers. AISTATS.
- Backman, T., Cao, Y., Girke, T. (n.d.). ChemMineTools. Retrieved October 21, 2015, from, http://chemmine.ucr.edu.
- Backman, T. W. H., Cao, Y., & Girke, T. (2011). ChemMine tools: An online service for analyzing and clustering small molecules. Nucleic Acids Research, 39(suppl), W486–W491. https://doi.org/10.1093/nar/gkr320
- Blanc, J., Geney, R., & Menet, C. (2013). Type II kinase inhibitors: An opportunity in cancer for rational design. Anti-Cancer Agents in Medicinal Chemistry, 13(5), 731–747. https://doi.org/10.2174/1871520611313050008
- Bonnet, P. (2012). Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists. European Journal of Medicinal Chemistry, 54, 679–689. https://doi.org/10.1016/j.ejmech.2012.06.024
- Boyle, N. M. O., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3(1), 1–14. https://doi.org/10.1186/1758-2946-3-33
- Brown, N. (2013). Identifying and representing scaffolds. Scaffold Hopping in Medicinal Chemistry, 58, 1–14. https://doi.org/10.1002/9783527665143.ch01
- Chen, C. Y.-C. (2011). TCM Database@Taiwan: The world’s largest traditional Chinese medicine database for drug screening in silico. PloS One, 6(1), e15939. https://doi.org/10.1371/journal.pone.0015939
- Chen, Y., de Bruyn Kops, C., & Kirchmair, J. (2019). Resources for chemical, biological, and structural data on natural products. Progress in the Chemistry of Organic Natural Products, 110, 37–71. https://doi.org/10.1007/978-3-030-14632-0_2
- Chohan, T. A., Qian, H., Pan, Y., & Chen, J. (2014). Cyclin-dependent Kinase-2 as a target for cancer therapy: Progress in the development of CDK2 inhibitors as anti-cancer agents. Current Medicinal Chemistry, 22(2), 237–263. https://doi.org/10.2174/0929867321666141106113633
- Cicenas, J., & Cicenas, E. (2016). Multi-kinase inhibitors, AURKs and cancer. Medical Oncology, 33(5), 1–11. https://doi.org/10.1007/s12032-016-0758-4
- Daina, A., & Zoete, V. (2016). A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11(11), 1117–1121. https://doi.org/10.1002/cmdc.201600182
- Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 1–13. https://doi.org/10.1038/srep42717
- Delaney, J. S. (2004). ESOL: Estimating aqueous solubility directly from molecular structure. Journal of Chemical Information and Computer Sciences, 44(3), 1000–1005. https://doi.org/10.1021/ci034243x
- Dong, X., Zhou, X., Jing, H., Chen, J., Liu, T., Yang, B., He, Q., & Hu, Y. (2011). Pharmacophore identification, virtual screening and biological evaluation of prenylated flavonoids derivatives as PKB/Akt1 inhibitors. European Journal of Medicinal Chemistry, 46(12), 5949–5958. https://doi.org/10.1016/j.ejmech.2011.10.006
- Dror, O., Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2009). Novel approach for efficient pharmacophore-based virtual screening: Method and applications. Journal of Chemical Information and Modeling, 49(10), 2333–2343. https://doi.org/10.1021/ci900263d
- Du, J., & Tang, X. L. (2014). Natural products against cancer: A comprehensive bibliometric study of the research projects, publications, patents and drugs. Journal of Cancer Research and Therapeutics, 10(5), 27–37. https://doi.org/10.4103/0973-1482.139750
- Dumas, J. (2002). Protein kinase inhibitors from the urea class. Current Opinion in Drug Discovery & Development, 5(5), 718–727.
- Egan, W. J., Merz, K. M., & Baldwin, J. J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867–3877. https://doi.org/10.1021/jm000292e
- Erlanson, D. a. (2006). Fragment-based lead discovery: A chemical update. Current Opinion in Biotechnology, 17(6), 643–652. https://doi.org/10.1016/j.copbio.2006.10.007
- Fatima, S., Gupta, P., Sharma, S., Sharma, A., & Agarwal, S. M. (2020). ADMET profiling of geographically diverse phytochemical using chemoinformatic tools. Future Medicinal Chemistry, 12(1), 69–87. https://doi.org/10.4155/fmc-2019-0206
- Ghose, A. K., Herbertz, T., Pippin, D. A., Salvino, J. M., & Mallamo, J. P. (2008). Knowledge based prediction of ligand binding modes and rational inhibitor design for kinase drug discovery. Journal of Medicinal Chemistry, 51(17), 5149–5171. https://doi.org/10.1021/jm800475y
- Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55–68. https://doi.org/10.1021/cc9800071
- Grabowski, K., Baringhaus, K., & Schneider, G. (2008). Scaffold diversity of natural products: Inspiration for combinatorial library design. Natural Product Reports, 25(5), 892–904. https://doi.org/10.1039/b715668p
- Gu, J., Gui, Y., Chen, L., Yuan, G., Lu, H., & Xu, X. (2013). Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One, 8(4), 1–10. https://doi.org/10.1371/journal.pone.0062839
- Guo, Z. (2016). The modification of natural products for medical use. Acta Pharmaceutica Sinica B 7(2), 119–136. https://doi.org/10.1016/j.apsb.2016.06.003
- Hajian-Tilaki, K. (2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian Journal of Internal Medicine, 4(2), 627–635. https://doi.org/10.1017/CBO9781107415324.004
- Harvey, A. L. (2008). Natural products in drug discovery. Drug Discovery Today, 13(19–20), 894–901. https://doi.org/10.1016/j.drudis.2008.07.004
- Hasegawa, M., Nishigaki, N., Washio, Y., Kano, K., Harris, P. A., Sato, H., Mori, I., West, R. I., Shibahara, M., Toyoda, H., Wang, L., Nolte, R. T., Veal, J. M., & Cheung, M. (2007). Discovery of novel benzimidazoles as potent inhibitors of TIE-2 and VEGFR-2 tyrosine kinase receptors. Journal of Medicinal Chemistry, 50(18), 4453–4470. https://doi.org/10.1021/jm0611051
- Hendlich, M., Bergner, A., Günther, J., & Klebe, G. (2003). Relibase: Design and development of a database for comprehensive analysis of protein-ligand interactions. Journal of Molecular Biology, 326(2), 607–620. https://doi.org/10.1016/S0022-2836(02)01408-0
- Hessler, G., & Baringhaus, K.-H. (2010). The scaffold hopping potential of pharmacophores. Drug Discovery Today: Technologies, 7(4), e263–e269. https://doi.org/10.1016/j.ddtec.2010.09.001
- Hopkins, A. L., Groom, C. R., & Alex, A. (2004). Ligand efficiency: A useful metric for lead selection. Drug Discovery Today, 9(10), 430–431. https://doi.org/10.1016/S1359-6446(04)03069-7
- Hopkins, A. L., Keserü, G. M., Leeson, P. D., Rees, D. C., & Reynolds, C. H. (2014). The role of ligand efficiency metrics in drug discovery. Nature Reviews Drug Discovery, 13(2), 105–121. https://doi.org/10.1038/nrd4163
- Hughes, S. J., Millan, D. S., Kilty, I. C., Lewthwaite, R. A., Mathias, J. P., O’Reilly, M. A., Pannifer, A., Phelan, A., Stühmeier, F., Baldock, D. A., & Brown, D. G. (2011). Fragment based discovery of a novel and selective PI3 kinase inhibitor. Bioorganic & Medicinal Chemistry Letters, 21(21), 6586–6590. https://doi.org/10.1016/j.bmcl.2011.07.117
- Hung, A. W., Ramek, A., Wang, Y., Kaya, T., Wilson, J. A., Clemons, P. A., & Young, D. W. (2011). Route to three-dimensional fragments using diversity-oriented synthesis. Proceedings of the National Academy of Sciences, 108(17), 6799–6804. https://doi.org/10.1073/pnas.1015271108
- Ilieva, Y., Kokanova-Nedialkova, Z., Nedialkov, P., & Momekov, G. (2018). In silico ADME and drug-likeness evaluation of a series of cytotoxic polyprenylated acylphloroglucinols, isolated from Hypericum annulatum Morris subsp. annulatum. Bulgarian Chemical Communications, 50, 193–199.
- Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., & Coleman, R. G. (2012). ZINC: A free tool to discover chemistry for biology. Journal of Chemical Information and Modeling, 52(7), 1757–1768. https://doi.org/10.1021/ci3001277
- Jarrahpour, A., Fathi, J., Mimouni, M., Hadda, T. B., Sheikh, J., Chohan, Z., & Parvez, A. (2012). Petra, Osiris and Molinspiration (POM) drug design: Antibacterial activity and biopharmaceutical characterization of some azo Schiff bases. Medicinal Chemistry Research, 21(8), 1984–1990. https://doi.org/10.1007/s00044-011-9723-0
- Jhoti, H., Williams, G., Rees, D. C., & Murray, C. W. (2013). The “rule of three” for fragment-based drug discovery: Where are we now? Nature Reviews Drug Discovery, 12(8), 644–644. https://doi.org/10.1038/nrd3926-c1
- Keserü, G. M., & Makara, G. M. (2009). The influence of lead discovery strategies on the properties of drug candidates. Nature Reviews Drug Discovery, 8(3), 203–212. https://doi.org/10.1038/nrd2796
- Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews Drug Discovery, 3(11), 935–949. https://doi.org/10.1038/nrd1549
- Klebe, G. (2006). Virtual ligand screening: Strategies, perspectives and limitations. Drug Discovery Today., 11(13-14), 580–594. https://doi.org/10.1016/j.drudis.2006.05.012
- Konteatis, Z. D. (2010). In silico fragment-based drug design. Expert Opinion on Drug Discovery, 5(11), 1047–1065. https://doi.org/10.1517/17460441.2010.523697
- Kooistra, A. J., Kanev, G. K., van Linden, O. P. J., Leurs, R., de Esch, I. J. P., & de Graaf, C. (2016). KLIFS: A structural kinase-ligand interaction database. Nucleic Acids Research, 44(D1), D365–71. https://doi.org/10.1093/nar/gkv1082
- Lanz, J., & Ried, R. (2015). Merging allosteric and active site binding motifs: De novo generation of target selectivity and potency via natural-product-derived fragments. ChemMedChem, 10(3), 451–454. https://doi.org/10.1002/cmdc.201402478
- Latham, A. M., Kankanala, J., Fearnley, G. W., Gage, M. C., Kearney, M. T., Homer-Vanniasinkam, S., Wheatcroft, S. B., Fishwick, C. W. G., & Ponnambalam, S. (2014). In silico design and biological evaluation of a dual specificity kinase inhibitor targeting cell cycle progression and angiogenesis. PloS One, 9(11), e110997. https://doi.org/10.1371/journal.pone.0110997
- Leach, A. R., & Hann, M. M. (2011). Molecular complexity and fragment-based drug discovery: Ten years on. Current Opinion in Chemical Biology, 15(4), 489–496. https://doi.org/10.1016/j.cbpa.2011.05.008
- Leelananda, S. P., & Lindert, S. (2016). Computational methods in drug discovery. Beilstein Journal of Organic Chemistry, 12, 2694–2718. https://doi.org/10.3762/bjoc.12.267
- Lewell, X. Q., Judd, D. B., Watson, S. P., & Hann, M. M. (1998). RECAP Retrosynthetic Combinatorial Analysis Procedure: A powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry. Journal of Chemical Information and Computer Sciences, 38(3), 511–522. https://doi.org/10.1021/ci970429i
- Lipinski, C. A. (2000). Drug-like properties and the causes of poor solubility and poor permeability. Journal of Pharmacological and Toxicological Methods, 44(1), 235–249. https://doi.org/10.1016/S1056-8719(00)00107-6
- Lipinski, C. a. (2004). Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technologies, 1(4), 337–341. https://doi.org/10.1016/j.ddtec.2004.11.007
- Liu, R., Yue, Z., Tsai, C. C., & Shen, J. (2019). Assessing lysine and cysteine reactivities for designing targeted covalent kinase inhibitors. Journal of the American Chemical Society, 141(16), 6553–6560. https://doi.org/10.1021/jacs.8b13248
- Lücking, U., Siemeister, G., Schäfer, M., Briem, H., Krüger, M., Lienau, P., & Jautelat, R. (2007). Macrocyclic aminopyrimidines as multitarget CDK and VEGF-R inhibitors with potent antiproliferative activities. ChemMedChem, 2(1), 63–77. https://doi.org/10.1002/cmdc.200600199
- Mali, S., Sawant, S., Chaudhari, H., & Mandewale, M. (2019). In silico appraisal, synthesis, antibacterial screening and DNA Cleavage for 1,2,5-thiadiazole Derivative. Current Computer-Aided Drug Design, 15(5), 445–455. https://doi.org/10.2174/1573409915666190206142756
- Mandal, S., Moudgil, M., & Mandal, S. K. (2009). Rational drug design. European Journal of Pharmacology, 625(1-3), 90–100. https://doi.org/10.1016/j.ejphar.2009.06.065
- Martin, Y. C. (2005). A Bioavailability Score. Journal of Medicinal Chemistry, 48(9), 3164–3170. https://doi.org/10.1021/jm0492002
- McTigue, M., Murray, B. W., Chen, J. H., Deng, Y.-L., Solowiej, J., & Kania, R. S. (2012). Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors. Proceedings of the National Academy of Sciences, 109(45), 18281–18289. https://doi.org/10.1073/pnas.1207759109
- Mills, J. E. J., & Dean, P. M. (1996). Three-dimensional hydrogen-bond geometry and probability information from a crystal survey. Journal of Computer-Aided Molecular Design, 10(6), 607–622. https://doi.org/10.1007/BF00134183
- Mishra, N., & Basu, A. (2013). Exploring different virtual screening strategies for acetylcholinesterase inhibitors. BioMed Research International, 2013(236850), 1–8. Retrieved from http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L38602781 https://doi.org/10.1155/2013/236850
- Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841–1846. https://doi.org/10.1021/jm015507e
- Ntie-Kang, F., Zofou, D., Babiaka, S. B., Meudom, R., Scharfe, M., Lifongo, L. L., Mbah, J. A., Mbaze, L. M., Sippl, W., & Efange, S. M. N. (2013). AfroDb: A select highly potent and diverse natural product library from African medicinal plants. PloS One., 8(10), e78085. https://doi.org/10.1371/journal.pone.0078085
- Olsson, A.-K., Dimberg, A., Kreuger, J., & Claesson-Welsh, L. (2006). VEGF receptor signalling - in control of vascular function. Nature Reviews Molecular Cell Biology, 7(5), 359–371. https://doi.org/10.1038/nrm1911
- Over, B., Wetzel, S., Grütter, C., Nakai, Y., Renner, S., Rauh, D., & Waldmann, H. (2013). Natural-product-derived fragments for fragment-based ligand discovery. Nature Chemistry, 5(1), 21–28. https://doi.org/10.1038/nchem.1506
- Perricone, U., Wieder, M., Seidel, T., Langer, T., Padova, A., Almerico, A. M., & Tutone, M. (2017). A molecular dynamics–shared pharmacophore approach to boost early-enrichment virtual screening: A case study on peroxisome proliferator-activated receptor α. ChemMedChem, 12(16), 1399–1407. https://doi.org/10.1002/cmdc.201600526
- Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., & Ferrin, T. E. (2004). UCSF Chimera–a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
- Prada-Gracia, D., Huerta-Yépez, S., & Moreno-Vargas, L. M. (2016). Application of computational methods for anticancer drug discovery, design, and optimization. Boletín Médico Del Hospital Infantil de México, 73(6), 411–423. https://doi.org/10.1016/j.bmhimx.2016.10.006
- Quan, C., Xiao, J., Liu, L., Duan, Q., Yuan, P., & Zhu, F. (2017). Protein kinases as tumor biomarkers and therapeutic targets. Current Pharmaceutical Design, 23(29), 1–17. https://doi.org/10.2174/1381612823666170720113216
- Rabiller, M., Getlik, M., Klüter, S., Richters, A., Tückmantel, S., Simard, J. R., & Rauh, D. (2010). Proteus in the world of proteins: Conformational changes in protein kinases. Archiv Der Pharmazie, 343(4), 193–206. https://doi.org/10.1002/ardp.201000028
- Ripphausen, P., Nisius, B., Peltason, L., & Bajorath, J. (2010). Quo vadis, virtual screening? A comprehensive survey of prospective applications. Journal of Medicinal Chemistry, 53(24), 8461–8467. https://doi.org/10.1021/jm101020z
- Ritchie, T. J., & Macdonald, S. J. F. (2009). The impact of aromatic ring count on compound developability - are too many aromatic rings a liability in drug design? Drug Discovery Today, 14(21-22), 1011–1020. https://doi.org/10.1016/j.drudis.2009.07.014
- Rizzi, A., & Fioni, A. (2008). Virtual screening using PLS discriminant analysis and ROC curve approach: An application study on PDE4 inhibitors. Journal of Chemical Information and Modeling, 48(8), 1686–1692. https://doi.org/10.1021/ci800072r
- Rodrigues, T., Reker, D., Schneider, P., Schneider, G. (2016). Counting on natural products for drug design. Nature Publishing Group, (April), submitted. https://doi.org/10.1038/nchem.2479
- Rollinger, J., Stuppner, H., & Langer, T. (2008). Virtual screening for the discovery of bioactive natural products. Progress in Drug Research, 65(211), 213–249.
- Roskoski, R. (2007). Vascular endothelial growth factor (VEGF) signaling in tumor progression. Critical Reviews in Oncology/Hematology, 62(3), 179–213. https://doi.org/10.1016/j.critrevonc.2007.01.006
- Roskoski, R. (2015). A historical overview of protein kinases and their targeted small molecule inhibitors. Pharmacological Research, 100, 1–23. https://doi.org/10.1016/j.phrs.2015.07.010
- Roskoski, R. (2016). Classification of small molecule protein kinase inhibitors based upon the structures of their drug-enzyme complexes. Pharmacological Research, 103, 26–48. https://doi.org/10.1016/j.phrs.2015.10.021
- Rueda, M., Bottegoni, G., & Abagyan, R. (2010). Recipes for the selection of experimental protein conformations for virtual screening. Journal of Chemical Information and Modeling, 50(1), 186–193. https://doi.org/10.1021/ci9003943
- Sánchez-Martínez, C., Gelbert, L. M., Lallena, M. J., & de Dios, A. (2015). Cyclin dependent kinase (CDK) inhibitors as anticancer drugs. Bioorganic & Medicinal Chemistry Letters, 25(17), 3420–3435. https://doi.org/10.1016/j.bmcl.2015.05.100
- Sander, T., Freyss, J., Von Korff, M., & Rufener, C. (2015). DataWarrior: An open-source program for chemistry aware data visualization and analysis. Journal of Chemical Information and Modeling, 55(2), 460–473. https://doi.org/10.1021/ci500588j
- Sanders, M. P. A., McGuire, R., Roumen, L., de Esch, I. J. P., de Vlieg, J., Klomp, J. P. G., & de Graaf, C. (2012). From the protein’s perspective: The benefits and challenges of protein structure-based pharmacophore modeling. MedChemComm, 3(1), 28–38. https://doi.org/10.1039/C1MD00210D
- Schade, M. (2007). Fragment-based lead discovery by NMR. In A. Rahman, G. W. Caldwell, M. I. Choudhary, & M. R. Player (Eds.), Frontiers in drug design and discovery (Vol. 3, pp. 105–119). Bentham Science Publishers Ltd.
- Scholz, A., Wagner, K., Welzel, M., Remlinger, F., Wiedenmann, B., Siemeister, G., Rosewicz, S., & Detjen, K. M. (2009). The oral multitarget tumour growth inhibitor, ZK 304709, inhibits growth of pancreatic neuroendocrine tumours in an orthotopic mouse model. Gut, 58(2), 261–270. https://doi.org/10.1136/gut.2007.146415
- Scior, T., Bender, A., Tresadern, G., Medina-Franco, J. L., Martínez-Mayorga, K., Langer, T., Cuanalo-Contreras, K., & Agrafiotis, D. K. (2012). Recognizing pitfalls in virtual screening: A critical review. Journal of Chemical Information and Modeling, 52(4), 867–881. https://doi.org/10.1021/ci200528d
- Scoffin, R., & Slater, M. (2015). Virtual elaboration of fragment ideas: Growing, merging and linking fragments with realistic chemistry. Drug Discovery, Development & Delivery, 7(2), 2–5.
- Shityakov, S., Broscheit, J., & Förster, C. (2012). α-Cyclodextrin dimer complexes of dopamine and levodopa derivatives to assess drug delivery to the central nervous system: ADME and molecular docking studies. International Journal of Nanomedicine, 7, 3211–3219. https://doi.org/10.2147/IJN.S31373
- Siemeister, G., Luecking, U., Wagner, C., Detjen, K., Mc Coy, C., & Bosslet, K. (2006). Molecular and pharmacodynamic characteristics of the novel multi-target tumor growth inhibitor ZK 304709. Biomedicine & Pharmacotherapy = Pharmacotherapy, 60(6), 269–272. https://doi.org/10.1016/j.biopha.2006.06.003
- Sotriffer, C. (2011). Virtual screening: Principles, challenges and practical guidelines. In C. Sotriffer (Ed.), Virtual screening. Wiley-VHC.
- Stierand, K., & Rarey, M. (2007). From modeling to medicinal chemistry: Automatic generation of two-dimensional complex diagrams. ChemMedChem, 2(6), 853–860. https://doi.org/10.1002/cmdc.200700010
- Stierand, K., & Rarey, M. (2010). Drawing the PDB: Protein-ligand complexes in two dimensions. ACS Medicinal Chemistry Letters, 1(9), 540–545. https://doi.org/10.1021/ml100164p
- Su, B., Huang, Y., Chang, C., Tu, Y., & Tseng, Y. J. (2013). Template-Based de Novo design for type II kinase inhibitors and its extented application to acetylcholinesterase inhibitors. Molecules, 18(11), 13487–13509. https://doi.org/10.3390/molecules181113487
- Tang, H.-C., & Chen, C. Y.-C. (2014). Drug design of cyclin-dependent kinase 2 inhibitor for melanoma from traditional Chinese medicine. BioMed Research International, 2014, 1–17. https://doi.org/10.1155/2014/798742
- Thangapandian, S., John, S., Sakkiah, S., & Lee, K. W. (2010). Ligand and structure based pharmacophore modeling to facilitate novel histone deacetylase 8 inhibitor design. European Journal of Medicinal Chemistry, 45(10), 4409–4417. https://doi.org/10.1016/j.ejmech.2010.06.024
- Tsai, J., Taylor, R., Chothia, C., & Gerstein, M. (1999). The packing density in proteins: Standard radii and volumes. Journal of Molecular Biology, 290(1), 253–266. https://doi.org/10.1006/jmbi.1999.2829
- Valli, M., Dos Santos, R. N., Figueira, L. D., Nakajima, C. H., Castro-Gamboa, I., Andricopulo, A. D., & Bolzani, V. S. (2013). Development of a natural products database from the biodiversity of Brazil. Journal of Natural Products, 76(3), 439–444. https://doi.org/10.1021/np3006875
- van Linden, O. P. J., Kooistra, A. J., Leurs, R., de Esch, I. J. P., & de Graaf, C. (2014). KLIFS: A knowledge-based structural database to navigate kinase-ligand interaction space. Journal of Medicinal Chemistry, 57(2), 249–277. https://doi.org/10.1021/jm400378w
- Veber, D. F., Johnson, S. R., Cheng, H., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615–2623. https://doi.org/10.1021/jm020017n
- von Korff, M., & Sander, T. (2012). About complexity and self-similarity of chemical structures in drug discovery. In Chaos and complex systems (pp. 301–306). https://doi.org/10.1007/978-3-642-33914-1
- Wallach, I. (2011). Pharmacophore inference and its application to computational drug discovery. Drug Development Research, 72(1), 17–25. https://doi.org/10.1002/ddr.20398
- Wang, F., Yang, W., & Hu, X. (2018). Discovery of high affinity receptors for dityrosine through inverse virtual screening and docking and molecular dynamics. International Journal of Molecular Sciences, 20(1), 115–119. https://doi.org/10.3390/ijms20010115
- Wolber, G., & Langer, T. (2005). LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. Journal of Chemical Information and Modeling, 45(1), 160–169. https://doi.org/10.1021/ci049885e
- Xin, L.-T., Liu, L., Shao, C.-L., Yu, R.-L., Chen, F.-L., Yue, S.-J., Wang, M., Guo, Z.-L., Fan, Y.-C., Guan, H.-S., & Wang, C.-Y. (2017). Discovery of DNA topoisomerase I inhibitors with low-cytotoxicity based on virtual screening from natural products. Marine Drugs, 15(7), 217. https://doi.org/10.3390/md15070217
- Yousuf, Z., Iman, K., Iftikhar, N., & Mirza, M. U. (2017). Structure-based virtual screening and molecular docking for the identification of potential multi-targeted inhibitors against breast cancer. Breast Cancer: Targets and Therapy, 9, 447–459. https://doi.org/10.2147/BCTT.S132074
- Yuan, H., Liu, H., Tai, W., Wang, F., Zhang, Y., Yao, S., Ran, T., Lu, S., Ke, Z., Xiong, X., Xu, J., Chen, Y., & Lu, T. (2013). Molecular modelling on small molecular CDK2 inhibitors: An integrated approach using a combination of molecular docking, 3D-QSAR and pharmacophore modelling. SAR and QSAR in Environmental Research, 24(10), 795–817. https://doi.org/10.1080/1062936X.2013.815655
- Zhang, J., Yang, P. L., & Gray, N. S. (2009). Targeting cancer with small molecule kinase inhibitors. Nature Reviews Cancer, 9(1), 28–39. https://doi.org/10.1038/nrc2559
- Zhang, Q., Zhang, X., & You, Q. (2016). Lead discovery of type II BRAF V600E inhibitors targeting the structurally validated DFG-Out conformation based upon selected fragments. Molecules, 21(7), 879. https://doi.org/10.3390/molecules21070879
- Zhu, T., Cao, S., Su, P.-C., Patel, R., Shah, D., Chokshi, H. B., Szukala, R., Johnson, M. E., & Hevener, K. E. (2013). Hit identification and optimization in virtual screening: Practical recommendations based upon a critical literature analysis. Journal of Medicinal Chemistry, 56(17), 6560–6572. https://doi.org/10.1021/jm301916b.Hit