97. Guner OF, Bowen JP (2013) Pharmacophore modeling for ADME. Curr Top Med Chem
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98. Yamashita F, Hashida M (2004) In silico approaches for predicting ADME properties of
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99. de Groot MJ, Ekins S (2002) Pharmacophore modeling of cytochromes P450. Adv Drug
Deliv Rev 54:367–383
100. Ekins S, de Groot MJ, Jones JP (2001) Pharmacophore and three-dimensional quantitative
structure activity relationship methods for modeling cytochrome p450 active sites. Drug
Metab Dispos 29:936–944
101. Sorich MJ, Miners JO, McKinnon RA et al (2004) Multiple pharmacophores for the
investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol
Pharmacol 65:301–308
102. Hu Y, Bajorath J (2010) Polypharmacology directed compound data mining: identification
of promiscuous chemotypes with different activity profiles and comparison to approved
drugs. J Chem Inf Model 50:2112–2118
103. Keiser MJ, Roth BL, Armbruster BN (2007) Relating protein pharmacology by ligand
chemistry. Nat Biotechnol 25:197–206
104. Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to multitarget drug design: current databases, methods and applications. J Proteomics 74:2554–2574
105. Xu Y, Liu X, Li S (2013) Combinatorial pharmacophore modeling of organic cation
transporter 2 (OCT2) inhibitors: insights into multiple inhibitory mechanisms. 10:4611–4619
106. Rollinger JM, Schuster D, Danzl B et al (2009) In silico target fishing for rationalized ligand
discovery exemplified on constituents of ruta graveolens. Planta Med 75:195–204
107. Tschinke V, Cohen NJ (1993) The NEWLEAD program: a new method for the design of
candidate structures from pharmacophoric hypotheses. Med Chem 36:3863–3870
108. Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of
enzyme inhibitors. J Comput Aided Mol Des 6:61–78
109. Roe D, Kuntz IJ (1995) BUILDER v.2: improving the chemistry of a de novo design
strategy. J Comput Aided Mol Des 9:269–282
110. Joseph-McCarthy D (1999) Computational approaches to structure-based ligand design.
Pharmacol Ther 84:179–191
111. Schneider G, Bohm HJ (2002) Virtual screening and fast automated docking methods. Drug
Discov Today 7:64–70
112. Scior T, Bender A, Tresadern G et al (2012) Recognizing pitfalls in virtual screening: a
critical review. J Chem Inf Model 52:867–881
113. Vancraenenbroeck R, De Raeymaecker J, Lobbestael E et al (2014) In silico, in vitro and
cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2 dephosphorylation to inhibitor activity on LRRK2. Front Mol Neurosci 7:51
114. Schomburg KT, Bietz S, Briem H et al (2014) Facing the challenges of structure-based target
prediction by inverse virtual screening. J Chem Inf Model 54:1676–1686
115. Kirchmair J, Wolber G, Laggner C et al (2006) Comparative performance assessment of the
conformational model generators omega and catalyst: a large-scale survey on the retrieval of
protein-bound ligand conformations. J Chem Inf Model 46:1848–1861
116. Kirchmair J, Laggner C, Wolber G et al (2005) Comparative analysis of protein-bound
ligand conformations with respect to catalyst’s conformational space subsampling
algorithms. J Chem Inf Model 45:422–430
117. Nagamani S, Gaur AS, Tanneeru K et al (2017) Molecular property diagnostic suite
(MPDS): development of disease-specific open source web portals for drug discovery.
SAR QSAR Environ Res https://doi.org/10.1080/1062936x.2017.1402819
Pharmacophore Modelling and Screening: Concepts, Recent …
53
13:1327–1342
98. Yamashita F, Hashida M (2004) In silico approaches for predicting ADME properties of
drugs. Drug Metab Pharmacokinet 19:327–338
99. de Groot MJ, Ekins S (2002) Pharmacophore modeling of cytochromes P450. Adv Drug
Deliv Rev 54:367–383
100. Ekins S, de Groot MJ, Jones JP (2001) Pharmacophore and three-dimensional quantitative
structure activity relationship methods for modeling cytochrome p450 active sites. Drug
Metab Dispos 29:936–944
101. Sorich MJ, Miners JO, McKinnon RA et al (2004) Multiple pharmacophores for the
investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol
Pharmacol 65:301–308
102. Hu Y, Bajorath J (2010) Polypharmacology directed compound data mining: identification
of promiscuous chemotypes with different activity profiles and comparison to approved
drugs. J Chem Inf Model 50:2112–2118
103. Keiser MJ, Roth BL, Armbruster BN (2007) Relating protein pharmacology by ligand
chemistry. Nat Biotechnol 25:197–206
104. Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to multitarget drug design: current databases, methods and applications. J Proteomics 74:2554–2574
105. Xu Y, Liu X, Li S (2013) Combinatorial pharmacophore modeling of organic cation
transporter 2 (OCT2) inhibitors: insights into multiple inhibitory mechanisms. 10:4611–4619
106. Rollinger JM, Schuster D, Danzl B et al (2009) In silico target fishing for rationalized ligand
discovery exemplified on constituents of ruta graveolens. Planta Med 75:195–204
107. Tschinke V, Cohen NJ (1993) The NEWLEAD program: a new method for the design of
candidate structures from pharmacophoric hypotheses. Med Chem 36:3863–3870
108. Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of
enzyme inhibitors. J Comput Aided Mol Des 6:61–78
109. Roe D, Kuntz IJ (1995) BUILDER v.2: improving the chemistry of a de novo design
strategy. J Comput Aided Mol Des 9:269–282
110. Joseph-McCarthy D (1999) Computational approaches to structure-based ligand design.
Pharmacol Ther 84:179–191
111. Schneider G, Bohm HJ (2002) Virtual screening and fast automated docking methods. Drug
Discov Today 7:64–70
112. Scior T, Bender A, Tresadern G et al (2012) Recognizing pitfalls in virtual screening: a
critical review. J Chem Inf Model 52:867–881
113. Vancraenenbroeck R, De Raeymaecker J, Lobbestael E et al (2014) In silico, in vitro and
cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2 dephosphorylation to inhibitor activity on LRRK2. Front Mol Neurosci 7:51
114. Schomburg KT, Bietz S, Briem H et al (2014) Facing the challenges of structure-based target
prediction by inverse virtual screening. J Chem Inf Model 54:1676–1686
115. Kirchmair J, Wolber G, Laggner C et al (2006) Comparative performance assessment of the
conformational model generators omega and catalyst: a large-scale survey on the retrieval of
protein-bound ligand conformations. J Chem Inf Model 46:1848–1861
116. Kirchmair J, Laggner C, Wolber G et al (2005) Comparative analysis of protein-bound
ligand conformations with respect to catalyst’s conformational space subsampling
algorithms. J Chem Inf Model 45:422–430
117. Nagamani S, Gaur AS, Tanneeru K et al (2017) Molecular property diagnostic suite
(MPDS): development of disease-specific open source web portals for drug discovery.
SAR QSAR Environ Res https://doi.org/10.1080/1062936x.2017.1402819
Pharmacophore Modelling and Screening: Concepts, Recent …
53