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Next generation 3D pharmacophore modeling

Advanced Review

David Schaller, Dora Šribar, Theresa Noonan, Lihua Deng, Trung Ngoc Nguyen, Szymon Pach, David Machalz, Marcel Bermudez, Gerhard Wolber

Published Online: Feb 26 2020

DOI: 10.1002/wcms.1468

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Abstract 3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Interactions

3D pharmacophore generation approaches based on the available data. 3D pharmacophores can be generated from either a set of known ligands, atomistic models of ligand‐macromolecular target complexes or the sole macromolecular (apo) target

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PyRod applied to the binding pocket of cyclin‐dependent kinase 2. (a) The protein environment of water molecules is analyzed to generate (b) dynamic molecular interaction fields (dMIFs) describing the pharmacophoric characteristics of the binding pocket, (c) which can be translated into pharmacophore features for virtual screening. Yellow—hydrophobic contact, green—hydrogen bond donor, red—hydrogen bond acceptor

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Dynophores (dynamic pharmacophores) unveil dynamic binding mode changes of the sphingosine‐1‐phosphate receptor ligand ML056 during a 100 ns MD simulation. The yellow point clouds indicate lipophilic contacts, the red and green features represent hydrogen bond acceptor or donor, respectively, and a positively charged area is shown as a blue point cloud. The percentages next to the features refer to their occurrence frequency during the simulation. In the example shown, a major part of the molecule remains in its initial orientation resulting in nearly sphere‐like distributions of the according feature point clouds (right part). The lipophilic tail is much more flexible within the binding site as indicated by the banana‐shaped feature cloud (left part). MD, molecular dynamics

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Discovery of covalent inhibitors of viral 3C protease. The initial fragment was identified with a 3D pharmacophore and further optimized by scaffold hopping and subsequent fragment growing. Green arrow—hydrogen bond donor, red arrow—hydrogen bond acceptor, yellow sphere—lipophilic contact, orange sphere—residue binding point

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Virtual screening workflow. 3D pharmacophores are generated with either structure‐ or ligand‐based approaches. State‐of‐the art retrospective validation is performed by plotting ROC curves with elaborated sets of actives and decoys. Pharmacophore‐based virtual screening is often followed by computationally more expensive methods such as docking or molecular dynamics simulations to get more differentiated structural insights. ROC, receiver operating characteristics

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References

Güner, OF, Bowen, JP. Setting the record straight: The origin of the pharmacophore concept. J Chem Inf Model. 2014;54(5):1269–1283. https://doi.org/10.1021/ci5000533
Reymond, J‐L, van Deursen, R, Blum, LC, Ruddigkeit, L. Chemical space as a source for new drugs. Med Chem Commun. 2010;1(1):30. https://doi.org/10.1039/c0md00020e
Gromski, PS, Henson, AB, Granda, JM, Cronin, L. How to explore chemical space using algorithms and automation. Nat Rev Chem. 2019;3(2):119–128. https://doi.org/10.1038/s41570-018-0066-y
Van Drie, JH. Generation of three‐dimensional pharmacophore models. Wiley Interdis Rev. 2013;3(5):449–464. https://doi.org/10.1002/wcms.1129
Voth, AR, Khuu, P, Oishi, K, Ho, PS. Halogen bonds as orthogonal molecular interactions to hydrogen bonds. Nat Chem. 2009;1(1):74–79. https://doi.org/10.1038/nchem.112
Lu, Y, Shi, T, Wang, Y, et al. Halogen bonding‐a novel interaction for rational drug design? J Med Chem. 2009;52(9):2854–2862. https://doi.org/10.1021/jm9000133
Wilcken, R, Zimmermann, MO, Lange, A, Joerger, AC, Boeckler, FM. Principles and applications of halogen bonding in medicinal chemistry and chemical biology. J Med Chem. 2013;56(4):1363–1388. https://doi.org/10.1021/jm3012068
Leach, AR, Gillet, VJ, Lewis, RA, Taylor, R. Three‐dimensional pharmacophore methods in drug discovery. J Med Chem. 2010;53(2):539–558. https://doi.org/10.1021/jm900817u
Baroni, M, Cruciani, G, Sciabola, S, Perruccio, F, Mason, JS. A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): Theory and application. J Chem Inf Model. 2007;47(2):279–294. https://doi.org/10.1021/ci600253e
Koes, DR, Camacho, CJ. Pharmer: Efficient and exact pharmacophore search. J Chem Inf Model. 2011;51(6):1307–1314. https://doi.org/10.1021/ci200097m
Wolber, G, Langer, T. LigandScout: 3‐D pharmacophores derived from protein‐bound ligands and their use as virtual screening filters. J Chem Inf Model. 2005;45(1):160–169. https://doi.org/10.1021/ci049885e
Barnum, D, Greene, J, Smellie, A, Sprague, P. Identification of common functional configurations among molecules. J Chem Inf Comput Sci. 1996;36(3):563–571. https://doi.org/10.1021/ci950273r
Chemical Computing Group. Molecular operating environment (MOE). Montreal, QC, Canada; 2010.
Dixon, SL, Smondyrev, AM, Knoll, EH, Rao, SN, Shaw, DE, Friesner, RA. 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. 2006;20(10–11):647–671. https://doi.org/10.1007/s10822-006-9087-6
Taminau, J, Thijs, G, De Winter, H. Pharao: Pharmacophore alignment and optimization. J Mol Graph Model. 2008;27(2):161–169. https://doi.org/10.1016/j.jmgm.2008.04.003
Certara. UNITY, SYBYL‐X. St. Louis, MO; 2013.
Cheeseright, TJ, Mackey, MD, Scoffin, RA. High content pharmacophores from molecular fields: A biologically relevant method for comparing and understanding ligands. Curr Comput Aided Drug Des. 2011;7(3):190–205. https://doi.org/10.2174/157340911796504314
Schuster, D, Wolber, G. Identification of bioactive natural products by pharmacophore‐based virtual screening. Curr Pharm Des. 2010;16(15):1666–1681. https://doi.org/10.2174/138161210791164072
Wolber, G, Dornhofer, AA, Langer, T. Efficient overlay of small organic molecules using 3D pharmacophores. J Comput Aided Mol Des. 2006;20(12):773–788. https://doi.org/10.1007/s10822-006-9078-7
Klebe, G, Abraham, U, Mietzner, T. Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem. 1994;37(24):4130–4146. https://doi.org/10.1021/jm00050a010
Cramer, RD, Patterson, DE, Bunce, JD. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc. 1988;110(18):5959–5967. https://doi.org/10.1021/ja00226a005
Friedrich, N‐O, de Bruyn, KC, Flachsenberg, F, Sommer, K, Rarey, M, Kirchmair, J. Benchmarking commercial conformer ensemble generators. J Chem Inf Model. 2017;57(11):2719–2728. https://doi.org/10.1021/acs.jcim.7b00505
Spitzer, GM, Wellenzohn, B, Laggner, C, Langer, T, Liedl, KR. DNA minor groove pharmacophores describing sequence specific properties. J Chem Inf Model. 2007;47(4):1580–1589. https://doi.org/10.1021/ci600500v
Friesner, RA, Murphy, RB, Repasky, MP, et al. Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein‐ligand complexes. J Med Chem. 2006;49(21):6177–6196. https://doi.org/10.1021/jm051256o
Salam, NK, Nuti, R, Sherman, W. Novel method for generating structure‐based pharmacophores using energetic analysis. J Chem Inf Model. 2009;49(10):2356–2368. https://doi.org/10.1021/ci900212v
Goodford, PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem. 1985;28(7):849–857. https://doi.org/10.1021/jm00145a002
von Itzstein, M, Wu, W‐Y, Kok, GB, et al. Rational design of potent sialidase‐based inhibitors of influenza virus replication. Nature. 1993;363(6428):418–423. https://doi.org/10.1038/363418a0
Morris, GM, Huey, R, Lindstrom, W, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–2791. https://doi.org/10.1002/jcc.21256
Mortier, J, Dhakal, P, Volkamer, A. Truly target‐focused pharmacophore modeling: A novel tool for mapping intermolecular surfaces. Molecules. 2018;23(8):1959. https://doi.org/10.3390/molecules23081959
Barillari, C, Marcou, G, Rognan, D. Hot‐spots‐guided receptor‐based pharmacophores (HS‐pharm): A knowledge‐based approach to identify ligand‐anchoring atoms in protein cavities and prioritize structure‐based pharmacophores. J Chem Inf Model. 2008;48(7):1396–1410. https://doi.org/10.1021/ci800064z
Hurst, T. Flexible 3D searching: The directed tweak technique. J Chem Inf Model. 1994;34(1):190–196. https://doi.org/10.1021/ci00017a025
Seidel, T, Ibis, G, Bendix, F, Wolber, G. Strategies for 3D pharmacophore‐based virtual screening. Drug Discov Today Technol. 2010;7(4):e221–e228. https://doi.org/10.1016/j.ddtec.2010.11.004
Hawkins, PCD. Conformation generation: The state of the art. J Chem Inf Model. 2017;57(8):1747–1756. https://doi.org/10.1021/acs.jcim.7b00221
Kirchmair, J, Distinto, S, Markt, P, et al. How to optimize shape‐based virtual screening: Choosing the right query and including chemical information. J Chem Inf Model. 2009;49(3):678–692. https://doi.org/10.1021/ci8004226
Kirchmair, J, Distinto, S, Schuster, D, Spitzer, G, Langer, T, Wolber, G. Enhancing drug discovery through in silico screening: Strategies to increase true positives retrieval rates. Curr Med Chem. 2008;15(20):2040–2053. https://doi.org/10.2174/092986708785132843
Kirchmair, J, Wolber, G, Laggner, C, Langer, T. 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. 2006;46(4):1848–1861. https://doi.org/10.1021/ci060084g
Sanders, MPA, Barbosa, AJM, Zarzycka, B, et al. Comparative analysis of pharmacophore screening tools. J Chem Inf Model. 2012;52(6):1607–1620. https://doi.org/10.1021/ci2005274
Mysinger, MM, Carchia, M, Irwin, JJ, Shoichet, BK. Directory of useful decoys, enhanced (DUD‐E): Better ligands and decoys for better benchmarking. J Med Chem. 2012;55(14):6582–6594. https://doi.org/10.1021/jm300687e
Ntie‐Kang, F, Simoben, CV, Karaman, B, et al. Pharmacophore modeling and in silico toxicity assessment of potential anticancer agents from African medicinal plants. Drug Des Devel Ther. 2016;10:2137–2154. https://doi.org/10.2147/DDDT.S108118
Braga, RC, Andrade, CH. Assessing the performance of 3D pharmacophore models in virtual screening: How good are they? Curr Top Med Chem. 2013;13(9):1127–1138. https://doi.org/10.2174/1568026611313090010
Triballeau, N, Acher, F, Brabet, I, Pin, J‐P, Bertrand, H‐O. Virtual screening workflow development guided by the “receiver operating characteristic” curve approach. Application to high‐throughput docking on metabotropic glutamate receptor subtype 4. J Med Chem. 2005;48(7):2534–2547. https://doi.org/10.1021/jm049092j
Kumar, H, Kawai, T, Akira, S. Toll‐like receptors and innate immunity. Biochem Biophys Res Commun. 2009;388(4):621–625. https://doi.org/10.1016/j.bbrc.2009.08.062
Murgueitio, MS, Rakers, C, Frank, A, Wolber, G. Balancing inflammation: Computational design of small‐molecule toll‐like receptor modulators. Trends Pharmacol Sci. 2017;38(2):155–168. https://doi.org/10.1016/j.tips.2016.10.007
Murgueitio, MS, Henneke, P, Glossmann, H, Santos‐Sierra, S, Wolber, G. Prospective virtual screening in a sparse data scenario: Design of small‐molecule TLR2 antagonists. ChemMedChem. 2014;9(4):813–822. https://doi.org/10.1002/cmdc.201300445
Grabowski, M, Murgueitio, MS, Bermudez, M, Rademann, J, Wolber, G, Weindl, G. Identification of a pyrogallol derivative as a potent and selective human TLR2 antagonist by structure‐based virtual screening. Biochem Pharmacol. 2018;154:148–160. https://doi.org/10.1016/j.bcp.2018.04.018
Grabowski, M, Murgueitio, MS, Bermudez, M, Wolber, G, Weindl, G. The novel small‐molecule antagonist MMG‐11 preferentially inhibits TLR2/1 signaling. Biochem Pharmacol. 2020;171:113687. https://doi.org/10.1016/j.bcp.2019.113687
Šribar, D, Grabowski, M, Murgueitio, MS, Bermudez, M, Weindl, G, Wolber, G. Identification and characterization of a novel chemotype for human TLR8 inhibitors. Eur J Med Chem. 2019;179:744–752. https://doi.org/10.1016/j.ejmech.2019.06.084
Singh, J, Petter, RC, Baillie, TA, Whitty, A. The resurgence of covalent drugs. Nat Rev Drug Discov. 2011;10(4):307–317. https://doi.org/10.1038/nrd3410
De Cesco, S, Kurian, J, Dufresne, C, Mittermaier, AK, Moitessier, N. Covalent inhibitors design and discovery. Eur J Med Chem. 2017;138:96–114. https://doi.org/10.1016/j.ejmech.2017.06.019
Johnson, DS, Weerapana, E, Cravatt, BF. Strategies for discovering and derisking covalent, irreversible enzyme inhibitors. Future Med Chem. 2010;2(6):949–964. https://doi.org/10.4155/fmc.10.21
Bauer, RA. Covalent inhibitors in drug discovery: From accidental discoveries to avoided liabilities and designed therapies. Drug Discov Today. 2015;20(9):1061–1073. https://doi.org/10.1016/j.drudis.2015.05.005
London, N, Miller, RM, Krishnan, S, et al. Covalent docking of large libraries for the discovery of chemical probes. Nat Chem Biol. 2014;10(12):1066–1072. https://doi.org/10.1038/nchembio.1666
Lonsdale, R, Burgess, J, Colclough, N, et al. Expanding the armory: Predicting and tuning covalent warhead reactivity. J Chem Inf Model. 2017;57(12):3124–3137. https://doi.org/10.1021/acs.jcim.7b00553
Schulz, R, Atef, A, Becker, D, et al. Phenylthiomethyl ketone‐based fragments show selective and irreversible inhibition of enteroviral 3C proteases. J Med Chem. 2018;61(3):1218–1230. https://doi.org/10.1021/acs.jmedchem.7b01440
Murray, CW, Rees, DC. The rise of fragment‐based drug discovery. Nat Chem. 2009;1(3):187–192. https://doi.org/10.1038/nchem.217
Hauser, AS, Attwood, MM, Rask‐Andersen, M, Schiöth, HB, Gloriam, DE. Trends in GPCR drug discovery: New agents, targets and indications. Nat Rev Drug Discov. 2017;16(12):829–842. https://doi.org/10.1038/nrd.2017.178
Frandsen, IO, Boesgaard, MW, Fidom, K, et al. Identification of histamine H3 receptor ligands using a new crystal structure fragment‐based method. Sci Rep. 2017;7(1):4829. https://doi.org/10.1038/s41598-017-05058-w
Fidom, K, Isberg, V, Hauser, AS, et al. A new crystal structure fragment‐based pharmacophore method for G protein‐coupled receptors. Methods. 2015;71:104–112. https://doi.org/10.1016/j.ymeth.2014.09.009
Schaller, D, Hagenow, S, Stark, H, Wolber, G. Ligand‐guided homology modeling drives identification of novel histamine H3 receptor ligands. PLoS One. 2019;14(6):e0218820. https://doi.org/10.1371/journal.pone.0218820
Temml, V, Garscha, U, Romp, E, et al. Discovery of the first dual inhibitor of the 5‐lipoxygenase‐activating protein and soluble epoxide hydrolase using pharmacophore‐based virtual screening. Sci Rep. 2017;7(1):42751. https://doi.org/10.1038/srep42751
Waltenberger, B, Garscha, U, Temml, V, et al. Discovery of potent soluble epoxide hydrolase (sEH) inhibitors by pharmacophore‐based virtual screening. J Chem Inf Model. 2016;56(4):747–762. https://doi.org/10.1021/acs.jcim.5b00592
Yao, T‐T, Fang, S‐W, Li, Z‐S, et al. Discovery of novel succinate dehydrogenase inhibitors by the integration of in silico library design and pharmacophore mapping. J Agric Food Chem. 2017;65(15):3204–3211. https://doi.org/10.1021/acs.jafc.7b00249
Rakers, C, Schumacher, F, Meinl, W, Glatt, H, Kleuser, B, Wolber, G. In silico prediction of human sulfotransferase 1E1 activity guided by pharmacophores from molecular dynamics simulations. J Biol Chem. 2016;291(1):58–71. https://doi.org/10.1074/jbc.M115.685610
Tkachenko, A, Bermudez, M, Irmer‐Stooff, S, et al. Nuclear transport of the human aryl hydrocarbon receptor and subsequent gene induction relies on its residue histidine 291. Arch Toxicol. 2018;92(3):1151–1160. https://doi.org/10.1007/s00204-017-2129-0
Bermudez, M, Rakers, C, Wolber, G. Structural characteristics of the allosteric binding site represent a key to subtype selective modulators of muscarinic acetylcholine receptors. Mol Inform. 2015;34(8):526–530. https://doi.org/10.1002/minf.201500025
Bock, A, Bermudez, M, Krebs, F, et al. Ligand binding ensembles determine graded agonist efficacies at a G protein‐coupled receptor. J Biol Chem. 2016;291(31):16375–16389. https://doi.org/10.1074/jbc.M116.735431
Bermudez, M, Bock, A, Krebs, F, et al. Ligand‐specific restriction of extracellular conformational dynamics constrains signaling of the M2 muscarinic receptor. ACS Chem Biol. 2017;12(7):1743–1748. https://doi.org/10.1021/acschembio.7b00275
Bermudez, M, Bock, A. Does divergent binding pocket closure drive ligand bias for class a GPCRs? Trends Pharmacol Sci. 2019;40(4):236–239. https://doi.org/10.1016/j.tips.2019.02.005
Bermudez, M, Nguyen, TN, Omieczynski, C, Wolber, G. Strategies for the discovery of biased GPCR ligands. Drug Discov Today. 2019;24(4):1031–1037. https://doi.org/10.1016/j.drudis.2019.02.010
Agnetta, L, Bermudez, M, Riefolo, F, et al. Fluorination of photoswitchable muscarinic agonists tunes receptor pharmacology and photochromic properties. J Med Chem. 2019;62(6):3009–3020. https://doi.org/10.1021/acs.jmedchem.8b01822
Hu, B, Lill, MA. Protein pharmacophore selection using hydration‐site analysis. J Chem Inf Model. 2012;52(4):1046–1060. https://doi.org/10.1021/ci200620h
Yu, W, Lakkaraju, SK, Raman, EP, MacKerell, AD. Site‐identification by ligand competitive saturation (SILCS) assisted pharmacophore modeling. J Comput Aided Mol Des. 2014;28(5):491–507. https://doi.org/10.1007/s10822-014-9728-0
Yu, W, Lakkaraju, SK, Raman, EP, Fang, L, MacKerell, AD. Pharmacophore modeling using site‐identification by ligand competitive saturation (SILCS) with multiple probe molecules. J Chem Inf Model. 2015;55(2):407–420. https://doi.org/10.1021/ci500691p
Sydow, D. Dynophores: Novel dynamic pharmacophores (Master Thesis). Humboldt‐Universität zu Berlin; 2015.
Wieder, M, Garon, A, Perricone, U, et al. Common hits approach: Combining pharmacophore modeling and molecular dynamics simulations. J Chem Inf Model. 2017;57(2):365–385. https://doi.org/10.1021/acs.jcim.6b00674
Perricone, U, Wieder, M, Seidel, T, et al. A molecular dynamics‐shared pharmacophore approach to boost early‐enrichment virtual screening: A case study on peroxisome proliferator‐activated receptor α. ChemMedChem. 2017;12(16):1399–1407. https://doi.org/10.1002/cmdc.201600526
Schuetz, DA, Seidel, T, Garon, A, et al. GRAIL: Grids of pharmacophore interaction fields. J Chem Theory Comput. 2018;14(9):4958–4970. https://doi.org/10.1021/acs.jctc.8b00495
Jung, SW, Kim, M, Ramsey, S, Kurtzman, T, Cho, AE. Water pharmacophore: Designing ligands using molecular dynamics simulations with water. Sci Rep. 2018;8(1):1–11. https://doi.org/10.1038/s41598-018-28546-z
Schaller, D, Pach, S, Wolber, G. PyRod: Tracing water molecules in molecular dynamics simulations. J Chem Inf Model. 2019;59(6):2818–2829. https://doi.org/10.1021/acs.jcim.9b00281
Arcon, JP, Modenutti, CP, Avendaño, D, et al. AutoDock bias: Improving binding mode prediction and virtual screening using known protein‐ligand interactions. Cowen L, editor. Bioinformatics. 2019;35(19):3836–3838. https://doi.org/10.1093/bioinformatics/btz152
Lee, JY, Krieger, JM, Li, H, Bahar, I. Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations. Protein Sci. 2020;29(1):76–86. https://doi.org/10.1002/pro.3732
Sato, T, Honma, T, Yokoyama, S. Combining machine learning and pharmacophore‐based interaction fingerprint for in silico screening. J Chem Inf Model. 2010;50(1):170–185. https://doi.org/10.1021/ci900382e
Jiménez, J, Doerr, S, Martínez‐Rosell, G, Rose, AS, De Fabritiis, G. DeepSite: Protein‐binding site predictor using 3D‐convolutional neural networks. Valencia a, editor. Bioinformatics. 2017;33(19):3036–3042. https://doi.org/10.1093/bioinformatics/btx350
Jiménez, J, Škalič, M, Martínez‐Rosell, G, De Fabritiis, G. K DEEP: Protein‐ligand absolute binding affinity prediction via 3D‐convolutional neural networks. J Chem Inf Model. 2018;58(2):287–296. https://doi.org/10.1021/acs.jcim.7b00650
Škalič, M, Varela‐Rial, A, Jiménez, J, Martínez‐Rosell, G, De Fabritiis, G. LigVoxel: Inpainting binding pockets using 3D‐convolutional neural networks. Valencia a, editor. Bioinformatics. 2019;35(2):243–250. https://doi.org/10.1093/bioinformatics/bty583
Škalič, M, Jiménez, J, Sabbadin, D, De Fabritiis, G. Shape‐based generative modeling for de novo drug design. J Chem Inf Model. 2019;59(3):1205–1214. https://doi.org/10.1021/acs.jcim.8b00706
Schneidman‐Duhovny, D, Dror, O, Inbar, Y, Nussinov, R, Wolfson, HJ. PharmaGist: A webserver for ligand‐based pharmacophore detection. Nucleic Acids Res. 2008;36:W223–W228. https://doi.org/10.1093/nar/gkn187
Wang, X, Shen, Y, Wang, S, et al. PharmMapper 2017 update: A web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017;45(W1):W356–W360. https://doi.org/10.1093/nar/gkx374
Sunseri, J, Koes, DR. Pharmit: Interactive exploration of chemical space. Nucleic Acids Res. 2016;44(W1):W442–W448. https://doi.org/10.1093/nar/gkw287
Koes, D, Khoury, K, Huang, Y, et al. Enabling large‐scale design, synthesis and validation of small molecule protein‐protein antagonists. PLoS One. 2012;7(3):e32839. https://doi.org/10.1371/journal.pone.0032839
Koes, DR, Camacho, CJ. ZINCPharmer: Pharmacophore search of the ZINC database. Nucleic Acids Res. 2012;40(W1):W409–W414. https://doi.org/10.1093/nar/gks378
Carlson, HA, Masukawa, KM, Rubins, K, et al. Developing a dynamic pharmacophore model for HIV‐1 integrase. J Med Chem. 2000;43(11):2100–2114. https://doi.org/10.1021/jm990322h
Damm, KL, Carlson, HA. Exploring experimental sources of multiple protein conformations in structure‐based drug design. J Am Chem Soc. 2007;129(26):8225–8235. https://doi.org/10.1021/ja0709728
Guvench, O, MacKerell, AD. Computational fragment‐based binding site identification by ligand competitive saturation. Jacobson MP, editor. PLoS Comput Biol. 2009;5(7):e1000435. https://doi.org/10.1371/journal.pcbi.1000435
Lanning, ME, Yu, W, Yap, JL, et al. Structure‐based design of N‐substituted 1‐hydroxy‐4‐sulfamoyl‐2‐naphthoates as selective inhibitors of the Mcl‐1 oncoprotein. Eur J Med Chem. 2016;113:273–292. https://doi.org/10.1016/j.ejmech.2016.02.006
Nizami, B, Sydow, D, Wolber, G, Honarparvar, B. Molecular insight on the binding of NNRTI to K103N mutated HIV‐1 RT: Molecular dynamics simulations and dynamic pharmacophore analysis. Mol Biosyst. 2016;12(11):3385–3395. https://doi.org/10.1039/C6MB00428H
Mortier, J, Prévost, JRC, Sydow, D, et al. Arginase structure and inhibition: Catalytic site plasticity reveals new modulation possibilities. Sci Rep. 2017;7(1):13616. https://doi.org/10.1038/s41598-017-13366-4
Bergant, K, Janežič, M, Valjavec, K, et al. Structure‐guided optimization of 4,6‐substituted‐1,3,5‐triazin‐2(1H)‐ones as catalytic inhibitors of human DNA topoisomerase IIα. Eur J Med Chem. 2019;175:330–348. https://doi.org/10.1016/j.ejmech.2019.04.055
Durairaj, P, Fan, L, Machalz, D, Wolber, G, Bureik, M. Functional characterization and mechanistic modeling of the human cytochrome P450 enzyme CYP4A22. FEBS Lett. 2019;593(16):2214–2225. https://doi.org/10.1002/1873-3468.13489
Naß, A, Schaller, D, Wolber, G. Assessment of flexible shape complementarity: New opportunities to explain and induce selectivity in ligands of protein tyrosine phosphatase 1B. Mol Inform. 2019;38(5):1800141. https://doi.org/10.1002/minf.201800141
Vitorović‐Todorović, MD, Worek, F, Perdih, A, Bauk, SĐ, Vujatović, TB, Cvijetić, IN. The in vitro protective effects of the three novel nanomolar reversible inhibitors of human cholinesterases against irreversible inhibition by organophosphorous chemical warfare agents. Chem Biol Interact. 2019;309:108714. https://doi.org/10.1016/j.cbi.2019.06.027
Seco, J, Luque, FJ, Barril, X. Binding site detection and druggability index from first principles. J Med Chem. 2009;52(8):2363–2371. https://doi.org/10.1021/jm801385d
Arcon, JP, Defelipe, LA, Lopez, ED, et al. Cosolvent‐based protein pharmacophore for ligand enrichment in virtual screening. J Chem Inf Model. 2019;59(8):3572–3583. https://doi.org/10.1021/acs.jcim.9b00371
Bakan, A, Nevins, N, Lakdawala, AS, Bahar, I. Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J Chem Theory Comput. 2012;8(7):2435–2447. https://doi.org/10.1021/ct300117j
Amaro, RE, Baudry, J, Chodera, J, et al. Ensemble docking in drug discovery. Biophys J. 2018;114(10):2271–2278. https://doi.org/10.1016/j.bpj.2018.02.038
Korb, O, Olsson, TSG, Bowden, SJ, et al. Potential and limitations of ensemble docking. J Chem Inf Model. 2012;52(5):1262–1274. https://doi.org/10.1021/ci2005934
Evangelista Falcon, W, Ellingson, SR, Smith, JC, Baudry, J. Ensemble docking in drug discovery: How many protein configurations from molecular dynamics simulations are needed to reproduce known ligand binding? J Phys Chem B. 2019;123(25):5189–5195. https://doi.org/10.1021/acs.jpcb.8b11491
Vamathevan, J, Clark, D, Czodrowski, P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov. 2019;18(6):463–477. https://doi.org/10.1038/s41573-019-0024-5
Sterling, T, Irwin, JJ. ZINC 15—Ligand discovery for everyone. J Chem Inf Model. 2015;55(11):2324–2337. https://doi.org/10.1021/acs.jcim.5b00559
Gaulton, A, Hersey, A, Nowotka, M, et al. The ChEMBL database in 2017. Nucleic Acids Res. 2017;45(D1):D945–D954. https://doi.org/10.1093/nar/gkw1074
Kim, S, Chen, J, Cheng, T, et al. PubChem 2019 update: Improved access to chemical data. Nucleic Acids Res. 2019;47(D1):D1102–D1109. https://doi.org/10.1093/nar/gky1033

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