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design strategies: applications using relibase. J Mol Biol 326:621–636
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genetic algorithm for flexible docking. J Mol Biol 267:727–748
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(2016) COMPASS II: extended coverage for polymer and drug-like molecule databases. J Mol
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169. Schärfer C, Schulz-Gasch T, Hert J, Heinzerling L, Schulz B, Inhester T, Stahl M, Rarey M
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molecules with machine learning. Cryst Growth Des 19:1903–1911
177. Rama Krishna G, Ukrainczyk M, Zeglinski J, Rasmuson ÅC (2018) Prediction of solid state
properties of cocrystals using artificial neural network modeling. Cryst Growth Des 18:133–
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178. Bryant MJ, Maloney AGP, Sykes RA (2018) Predicting mechanical properties of crystalline
materials through topological analysis. CrystEngComm 20:2698–2704
179. Wang C, Sun CC (2019) Computational techniques for predicting mechanical properties of
organic crystals: a systematic evaluation. Mol Pharm 16:1732–1741
180. Pudasaini N, Upadhyay PP, Parker CR, Hagen SU, Bond AD, Rantanen J (2017) Downstream
processability of crystal habit-modified active pharmaceutical ingredient. Org Process Res Dev
21:571–577
181. Turner TD, Hatcher LE, Wilson CC, Roberts KJ (2019) Habit modification of the active
pharmaceutical ingredient lovastatin through a predictive solvent selection approach. J Pharm
Sci 108:1779–1787
182. Hooper D, Clarke FC, Docherty R, Mitchell J, Snowden MJ (2017) Effects of crystal habit on
the sticking propensity of ibuprofen—a case study. Int J Pharm 531:266–275
183. Chung YG, Camp J, Haranczyk M, Sikora BJ, Bury W, Krungleviciute V, Yildirim T, Farha
OK, Sholl DS, Snurr RQ (2014) Computation-ready, experimental metal–organic frameworks:
a tool to enable high-throughput screening of nanoporous crystals. Chem Mater 26:6185–6192
184. First EL, Floudas CA (2013) MOFomics: computational pore characterization of metalorganic frameworks. Microporous Mesoporous Mater 165:32–39
138
S. J. Coles et al.
complex three-dimensional interaction patterns including crystallographic packing effects.
Biopolymers 61:99–110
164. Hendlich M, Bergner A, Günther J, Klebe G (2003) Relibase: design and development of a
database for comprehensive analysis of protein–ligand interactions. J Mol Biol 326:607–620
165. Günther J, Bergner A, Hendlich M, Klebe G (2003) Utilising structural knowledge in drug
design strategies: applications using relibase. J Mol Biol 326:621–636
166. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a
genetic algorithm for flexible docking. J Mol Biol 267:727–748
167. Sun H, Jin Z, Yang C, Akkermans RLC, Robertson SH, Spenley NA, Miller S, Todd SM
(2016) COMPASS II: extended coverage for polymer and drug-like molecule databases. J Mol
Model 22:47
168. Vermaas JV, Petridis L, Ralph J, Crowley MF, Beckham GT (2019) Systematic parameterization of lignin for the CHARMM force field. Green Chem 21:109–122
169. Schärfer C, Schulz-Gasch T, Hert J, Heinzerling L, Schulz B, Inhester T, Stahl M, Rarey M
(2013) Inside cover: CONFECT: conformations from an expert collection of torsion patterns
(ChemMedChem 10/2013). ChemMedChem 8:1574–1574
170. Kothiwale S, Mendenhall JL, Meiler J (2015) BCL::Conf: small molecule conformational
sampling using a knowledge based rotamer library. J Cheminform 7:47
171. Korb O, Kuhn B, Hert J, Taylor N, Cole J, Groom C, Stahl M (2016) Interactive and versatile
navigation of structural databases. J Med Chem 59:4257–4266
172. Groom CR, Olsson TSG, Liebeschuetz JW, Bardwell DA, Bruno IJ, Allen FH (2012) Mining
the Cambridge Structural Database for bioisosteres. In: Bioisosteres medicinal chemistry.
Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 75–101
173. Cresset (2020) Fragments and conformations from the CCDC’s Cambridge Structural Database accessible through Cresset’s Spark. https://www.cresset-group.com/about/news/frag
ments-and-conformations-from-the-ccdcs-cambrid/. Accessed 4 July 2019
174. Galek PTA, Pidcock E, Wood PA, Bruno IJ, Groom CR (2012) One in half a million: a solid
form informatics study of a pharmaceutical crystal structure. CrystEngComm 14:2391–2403
175. Takieddin K, Khimyak YZ, Fábián L (2016) Prediction of hydrate and solvate formation using
statistical models. Cryst Growth Des 16:70–81
176. Xin D, Gonnella NC, He X, Horspool K (2019) Solvate prediction for pharmaceutical organic
molecules with machine learning. Cryst Growth Des 19:1903–1911
177. Rama Krishna G, Ukrainczyk M, Zeglinski J, Rasmuson ÅC (2018) Prediction of solid state
properties of cocrystals using artificial neural network modeling. Cryst Growth Des 18:133–
144
178. Bryant MJ, Maloney AGP, Sykes RA (2018) Predicting mechanical properties of crystalline
materials through topological analysis. CrystEngComm 20:2698–2704
179. Wang C, Sun CC (2019) Computational techniques for predicting mechanical properties of
organic crystals: a systematic evaluation. Mol Pharm 16:1732–1741
180. Pudasaini N, Upadhyay PP, Parker CR, Hagen SU, Bond AD, Rantanen J (2017) Downstream
processability of crystal habit-modified active pharmaceutical ingredient. Org Process Res Dev
21:571–577
181. Turner TD, Hatcher LE, Wilson CC, Roberts KJ (2019) Habit modification of the active
pharmaceutical ingredient lovastatin through a predictive solvent selection approach. J Pharm
Sci 108:1779–1787
182. Hooper D, Clarke FC, Docherty R, Mitchell J, Snowden MJ (2017) Effects of crystal habit on
the sticking propensity of ibuprofen—a case study. Int J Pharm 531:266–275
183. Chung YG, Camp J, Haranczyk M, Sikora BJ, Bury W, Krungleviciute V, Yildirim T, Farha
OK, Sholl DS, Snurr RQ (2014) Computation-ready, experimental metal–organic frameworks:
a tool to enable high-throughput screening of nanoporous crystals. Chem Mater 26:6185–6192
184. First EL, Floudas CA (2013) MOFomics: computational pore characterization of metalorganic frameworks. Microporous Mesoporous Mater 165:32–39
138
S. J. Coles et al.