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data bank. Structure 25:1916–1927. https://doi.org/10.1016/j.str.2017.10.009
181. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4:
automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791.
https://doi.org/10.1002/jcc.21256
182. Van Zundert GCP, Rodrigues JPGLM, Trellet M, Schmitz C (2016) The HADDOCK2.
2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol
428:720–725. https://doi.org/10.1016/j.jmb.2015.09.014
183. Diller DJ, Merz KM (2001) High throughput docking for library design and library
prioritization. Proteins Struct Funct Genet 43:113–124. https://doi.org/10.1002/1097-0134
(20010501)43:2%3c113:aid-prot1023%3e3.0.co;2-t
184. Venkatachalam CM, Jiang X, Oldfield T, Waldman M (2003) LigandFit: a novel method for
the shape-directed rapid docking of ligands to protein active sites. J Mol Graph Model
21:289–307. https://doi.org/10.1016/s1093-3263(02)00164-x
185. Holton S, Merckx A, Burgess D et al (2003) Structures of P. falciparum PfPK5 test the CDK
regulation paradigm and suggest mechanisms of small molecule inhibition.
Structure 11:1329–1337. https://doi.org/10.1016/j.str.2003.09.020
186. Engels MFM, Gibbs AC, Jaeger EP et al (2006) A cluster-based strategy for assessing the
overlap between large chemical libraries and its application to a recent acquisition. J Chem
Inf Model 46:2651–2660. https://doi.org/10.1021/ci600219n
187. Krier M, Bret G, Rognan D (2006) Assessing the scaffold diversity of screening libraries.
J Chem Inf Model 46:512–524. https://doi.org/10.1021/ci050352v
188. McGregor MJ, Muskal SM (1999) Pharmacophore fingerprinting. 1. Application to QSAR
and focused library design. J Chem Inf Comput Sci 39:569–574. https://doi.org/10.1021/
ci980159j
189. Reymond J-L, van Deursen R, Blum LC, Ruddigkeit L (2010) Chemical space as a source
for new drugs. Medchemcomm 1:30. https://doi.org/10.1039/c0md00020e
190. Williams AJ (2008) A perspective of publicly accessible/open-access chemistry databases.
Drug Discov Today 13:495–501. https://doi.org/10.1016/j.drudis.2008.03.017
191. Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical
review. Curr Med Chem 20:2839–2860. https://doi.org/10.2174/09298673113209990001
192. Hann MM, Oprea TI (2004) Pursuing the leadlikeness concept in pharmaceutical research.
Curr Opin Chem Biol 8:255–263. https://doi.org/10.1016/j.cbpa.2004.04.003
193. Villoutreix Bruno O, Renault Nicolas, Lagorce David et al (2007) Free resources to assist
structure-based virtual ligand screening experiments. Curr Protein Pept Sci 8:381–411.
https://doi.org/10.2174/138920307781369391
194. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational
approaches to estimate solubility and permeability in drug discovery and development
settings. Adv Drug Deliv Rev 64:4–17. https://doi.org/10.1016/j.addr.2012.09.019
195. Congreve M, Carr R, Murray C, Jhoti H (2003) A “rule of three” for fragment-based lead
discovery? Drug Discov Today 8:876–877. https://doi.org/10.1016/s1359-6446(03)02831-9
196. Hughes JD, Blagg J, Price DA et al (2008) Physiochemical drug properties associated with
in vivo toxicological outcomes. Bioorg Med Chem Lett 18:4872–4875. https://doi.org/10.
1016/j.bmcl.2008.07.071
197. Lagarde N, Zagury J-F, Montes M (2015) Benchmarking data sets for the evaluation of
virtual ligand screening methods: review and perspectives. J Chem Inf Model 55:1297–
1307. https://doi.org/10.1021/acs.jcim.5b00090
198. Réau M, Langenfeld F, Zagury J-F et al (2018) Decoys selection in benchmarking datasets:
overview and perspectives. Front Pharmacol 9 https://doi.org/10.3389/fphar.2018.00011
199. Clark RD, Strizhev A, Leonard JM et al (2002) Consensus scoring for ligand/protein
interactions. J Mol Graph Model 20:281–295
170
S. K. Panday and I. Ghosh