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tuberculosis GuaB2 identified by a target based high-throughput phenotypic screen. Sci Rep
6:1–10. https://doi.org/10.1038/srep38986
201. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol
11:494–502. https://doi.org/10.1016/j.cbpa.2007.08.033
202. Kumar A, Zhang KYJ (2018) A cross docking pipeline for improving pose prediction and
virtual screening performance. J Comput Aided Mol Des 32:163–173. https://doi.org/10.
1007/s10822-017-0048-z
203. Thilagavathi R, Mancera RL (2010) Ligand- protein cross-docking with water molecules.
J Chem Inf Model 50:415–421
204. Kilambi KP, Gray JJ (2017) Structure-based cross-docking analysis of antibody–antigen
interactions. Sci Rep 7:8145
205. Kroemer RT, Vulpetti A, McDonald JJ et al (2004) Assessment of docking poses:
interactions-based accuracy classification (IBAC) versus crystal structure deviations. J Chem
Inf Comput Sci 44:871–881
206. Smith RD, Dunbar JB, Ung PM et al (2011) CSAR benchmark exercise of 2010: combined
evaluation across all submitted scoring functions. J Chem Inf Model 51:2115–2131. https://
doi.org/10.1021/ci200269q
207. Oda A, Tsuchida K, Takakura T et al (2006) Comparison of consensus scoring strategies for
evaluating computational models of protein-ligand complexes. J Chem Inf Model 46:380–
391. https://doi.org/10.1021/ci050283k
208. Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore
perception, 3D QSAR model development, and 3D database screening: 1. Methodology and
preliminary results. J Comput Aided Mol Des 20:647–671. https://doi.org/10.1007/s10822006-9087-6
209. Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design—a review. Curr Top
Med Chem 10:95–115. https://doi.org/10.2174/156802610790232260
210. Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate
docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–
1759. https://doi.org/10.1021/jm030644s
211. Hawkins PCD, Skillman AG, Nicholls A (2007) Comparison of shape-matching and
docking as virtual screening tools. J Med Chem 50:74–82. https://doi.org/10.1021/
jm0603365
212. Hawkins PCD, Skillman AG, Warren GL et al (2010) Conformer generation with OMEGA:
algorithm and validation using high quality structures from the protein databank and
Cambridge structural database. J Chem Inf Model 50:572–584. https://doi.org/10.1021/
ci100031x
213. Takagi T, Amano M, Tomimoto M (2009) Novel method for the evaluation of 3D
conformation generators. J Chem Inf Model 49:1377–1388. https://doi.org/10.1021/
ci800393w
214. Ebejer J-P, Morris GM, Deane CM (2012) Freely available conformer generation methods:
how good are they? J Chem Inf Model 52:1146–1158. https://doi.org/10.1021/ci2004658
215. Loferer MJ, Kolossváry I, Aszódi A (2007) Analyzing the performance of conformational
search programs on compound databases. J Mol Graph Model 25:700–710. https://doi.org/
10.1016/j.jmgm.2006.05.008
216. Vainio MJ, Johnson MS (2007) Generating conformer ensembles using a multiobjective
genetic algorithm. J Chem Inf Model 47:2462–2474. https://doi.org/10.1021/ci6005646
217. Li J, Ehlers T, Sutter J et al (2007) CAESAR: a new conformer generation algorithm based
on recursive buildup and local rotational symmetry consideration. J Chem Inf Model
47:1923–1932. https://doi.org/10.1021/ci700136x
218. Watts KS, Dalal P, Murphy RB et al (2010) ConfGen: a conformational search method for
efficient generation of bioactive conformers. J Chem Inf Model 50:534–546. https://doi.org/
10.1021/ci100015j
In Silico Structure-Based Prediction of Receptor–Ligand Binding …
171