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Next generation 3D pharmacophore modeling

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Schaller, David, Šribar, Dora, Noonan, Theresa, Deng, Lihua, Nguyen, Trung Ngoc, Pach, Szymon, Machalz, David, Bermudez, Marcel, Wolber, Gerhard·2020·WIREs Computational Molecular Science

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

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AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 25,486
Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes
Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 6,851
Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931
PubChem 2019 update: improved access to chemical data
Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
A computational procedure for determining energetically favorable binding sites on biologically important macromolecules
Goodford, P. J. (1985) · Journal of Medicinal Chemistry · doi:10.1021/jm00145a002 · Cited by 2,634
Trends in GPCR drug discovery: new agents, targets and indications
Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B., & Gloriam, D. E. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.178 · Cited by 2,627
Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.
Mysinger, M. M., Carchia, M., Irwin, J. J., & Shoichet, B. K. (2012) · Journal of Medicinal Chemistry · doi:10.1021/jm300687e · Cited by 2,412
The ChEMBL database in 2017
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J., Mendez, D., Mutowo, P., Atkinson, F., Bellis, L. J., Cibrián-Uhalte, E., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,330
LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters
Wolber, G., & Langer, T. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049885e · Cited by 1,954
Self-citation
The resurgence of covalent drugs
Singh, J., Petter, R. C., Baillie, T. A., & Whitty, A. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3410 · Cited by 1,873 · cited 1×
“48,49 Prolonged residence time on targets and pharmacodynamic effects independent from pharmacokinetics allow for lower doses or longer dosage intervals, making covalently binding drugs attractive for many therapies.”
Background
Rational design of potent sialidase-based inhibitors of influenza virus replication
von Itzstein, M., Wu, W.-Y., Kok, G. B., Pegg, M. S., Dyason, J. C., Jin, B., Van Phan, T., Smythe, M. L., White, H. F., Oliver, S. W., et al. (1993) · Nature · doi:10.1038/363418a0 · Cited by 1,862
Molecular Similarity Indices in a Comparative Analysis (CoMSIA) of Drug Molecules to Correlate and Predict Their Biological Activity
Klebe, G., Abraham, U., & Mietzner, T. (1994) · Journal of Medicinal Chemistry · doi:10.1021/jm00050a010 · Cited by 1,816
PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database
Wang, X., Shen, Y., Wang, S., Li, S., Zhang, W., Liu, X., Lai, L., Pei, J., & Li, H. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx374 · Cited by 1,588
Principles and Applications of Halogen Bonding in Medicinal Chemistry and Chemical Biology
Wilcken, R., Zimmermann, M. O., Lange, A., Joerger, A. C., & Boeckler, F. M. (2013) · Journal of Medicinal Chemistry · doi:10.1021/jm3012068 · Cited by 1,331
PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results
Dixon, S. L., Smondyrev, A. M., Knoll, E. H., Rao, S. N., Shaw, D. E., & Friesner, R. A. (2006) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9087-6 · Cited by 1,198
Toll-like receptors and innate immunity
Kumar, H., Kawai, T., & Akira, S. (2009) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2009.08.062 · Cited by 1,173
KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
Jiménez, J., Škalič, M., Martínez-Rosell, G., & De Fabritiis, G. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00650 · Cited by 926
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., & De Fabritiis, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx350 · Cited by 808
The rise of fragment-based drug discovery
Murray, C. W., & Rees, D. C. (2009) · Nature Chemistry · doi:10.1038/nchem.217 · Cited by 791
Virtual Screening Workflow Development Guided by the “Receiver Operating Characteristic” Curve Approach. Application to High-Throughput Docking on Metabotropic Glutamate Receptor Subtype 4
Triballeau, N., Acher, F., Brabet, I., Pin, J.-P., & Bertrand, H.-O. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm049092j · Cited by 631
Halogen Bonding—A Novel Interaction for Rational Drug Design?
Lu, Y., Shi, T., Wang, Y., Yang, H., Yan, X., Luo, X., Jiang, H., & Zhu, W. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm9000133 · Cited by 559
Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies
Bauer, R. A. (2015) · Drug Discovery Today · doi:10.1016/j.drudis.2015.05.005 · Cited by 534
Ensemble Docking in Drug Discovery
Amaro, R. E., Baudry, J., Chodera, J., Demir, Ö., McCammon, J. A., Miao, Y., & Smith, J. C. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.02.038 · Cited by 479
A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application
Baroni, M., Cruciani, G., Sciabola, S., Perruccio, F., & Mason, J. S. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600253e · Cited by 439
ZINCPharmer: pharmacophore search of the ZINC database
Koes, D. R., & Camacho, C. J. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks378 · Cited by 436
Pharmit: interactive exploration of chemical space
Sunseri, J., & Koes, D. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw287 · Cited by 420
Halogen bonds as orthogonal molecular interactions to hydrogen bonds
Voth, A. R., Khuu, P., Oishi, K., & Ho, P. S. (2009) · Nature Chemistry · doi:10.1038/nchem.112 · Cited by 411
Strategies for Discovering and Derisking Covalent, Irreversible Enzyme Inhibitors
Johnson, D. S., Weerapana, E., & Cravatt, B. F. (2010) · Future Medicinal Chemistry · doi:10.4155/fmc.10.21 · Cited by 387
Chemical space as a source for new drugs
Reymond, J.-L., van Deursen, R., Blum, L. C., & Ruddigkeit, L. (2010) · MedChemComm · doi:10.1039/c0md00020e · Cited by 347
Novel Method for Generating Structure-Based Pharmacophores Using Energetic Analysis
Salam, N. K., Nuti, R., & Sherman, W. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900212v · Cited by 323
Identification of Common Functional Configurations Among Molecules
Barnum, D., Greene, J., Smellie, A., & Sprague, P. (1996) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci950273r · Cited by 319
Covalent inhibitors design and discovery.
De Cesco, S., Kurian, J., Dufresne, C., Mittermaier, A. K., & Moitessier, N. (2017) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2017.06.019 · Cited by 315
Efficient overlay of small organic molecules using 3D pharmacophores
Wolber, G., Dornhofer, A. A., & Langer, T. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9078-7 · Cited by 312
Self-citation
Covalent docking of large libraries for the discovery of chemical probes
London, N., Miller, R. M., Krishnan, S., Uchida, K., Irwin, J. J., Eidam, O., Gibold, L., Cimermančič, P., Bonnet, R., Shoichet, B. K., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1666 · Cited by 274
Computational Fragment-Based Binding Site Identification by Ligand Competitive Saturation
Guvench, O., & MacKerell, A. D. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000435 · Cited by 271
Developing a Dynamic Pharmacophore Model for HIV-1 Integrase
Carlson, H. A., Masukawa, K. M., Rubins, K., Bushman, F. D., Jorgensen, W. L., Lins, R. D., Briggs, J. M., & McCammon, J. A. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm990322h · Cited by 259
How to explore chemical space using algorithms and automation
Gromski, P. S., Henson, A. B., Granda, J. M., & Cronin, L. (2019) · Nature Reviews Chemistry · doi:10.1038/s41570-018-0066-y · Cited by 245
Binding Site Detection and Druggability Index from First Principles
Seco, J., Luque, F. J., & Barril, X. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm801385d · Cited by 239
Conformation Generation: The State of the Art
Hawkins, P. C. D. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00221 · Cited by 238
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229
How To Optimize Shape-Based Virtual Screening: Choosing the Right Query and Including Chemical Information
Kirchmair, J., Distinto, S., Markt, P., Schuster, D., Spitzer, G. M., Liedl, K. R., & Wolber, G. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8004226 · Cited by 193
Self-citation
Expanding the Armory: Predicting and Tuning Covalent Warhead Reactivity
Lonsdale, R., Burgess, J., Colclough, N., Davies, N. L., Lenz, E. M., Orton, A. L., & Ward, R. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00553 · Cited by 190
Flexible 3D searching: The directed tweak technique
Hurst, T. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00017a025 · Cited by 188
Pharmer: Efficient and Exact Pharmacophore Search
Koes, D. R., & Camacho, C. J. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200097m · Cited by 172
Potential and Limitations of Ensemble Docking
Korb, O., Olsson, T. S. G., Bowden, S. J., Hall, R. J., Verdonk, M. L., Liebeschuetz, J. W., & Cole, J. C. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2005934 · Cited by 169
Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules
Bakan, A., Nevins, N., Lakdawala, A. S., & Bahar, I. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300117j · Cited by 168
Comparative Performance Assessment of the Conformational Model Generators Omega and Catalyst: A Large-Scale Survey on the Retrieval of Protein-Bound Ligand Conformations
Kirchmair, J., Wolber, G., Laggner, C., & Langer, T. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060084g · Cited by 154
Self-citation
Pharao: Pharmacophore alignment and optimization
Taminau, J., Thijs, G., & Winter, H. L. D. (2008) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2008.04.003 · Cited by 141
Exploring Experimental Sources of Multiple Protein Conformations in Structure-Based Drug Design
Damm‐Ganamet, K. L., & Carlson, H. A. (2007) · Journal of the American Chemical Society · doi:10.1021/ja0709728 · Cited by 138
Strategies for 3D pharmacophore-based virtual screening
Seidel, T., Ibis, G., Bendix, F., & Wolber, G. (2010) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2010.11.004 · Cited by 137
Self-citation
Comparative Performance Assessment of the Conformational Model Generators Omega and Catalyst: A Large‐Scale Survey on the Retrieval of Protein‐Bound Ligand Conformations.
Kirchmaier, J., Wolber, G., Laggner, C., & Langer, T. (2006) · ChemInform · doi:10.1002/chin.200642205 · Cited by 134
Self-citation
Combining Machine Learning and Pharmacophore-Based Interaction Fingerprint for in Silico Screening
Sato, T., Honma, T., & Yokoyama, S. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900382e · Cited by 123
Comparative Analysis of Pharmacophore Screening Tools
Sanders, M. P. A., Barbosa, A. J. M., Zarzycka, B. A., Nicolaes, G. A. F., Klomp, J. P. G., de Vlieg, J., & Río, A. D. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci2005274 · Cited by 118
Assessing the Performance of 3D Pharmacophore Models in Virtual Screening: How Good are They?
Braga, R. C., & Andrade, C. H. (2013) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026611313090010 · Cited by 114
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
Barillari, C., Marcou, G., & Rognan, D. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800064z · Cited by 111
Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists
Koes, D. R., Khoury, K., Huang, Y., Wang, W., Biśta, M., Popowicz, G. M., Wolf, S., Holak, T. A., Dömling, A. S. S., & Camacho, C. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032839 · Cited by 105
Ligand Binding Ensembles Determine Graded Agonist Efficacies at a G Protein-coupled Receptor
Böck, A., Bermúdez, M., Krebs, F., Matera, C., Chirinda, B., Sydow, D., Dallanoce, C., Holzgrabe, U., Amici, M. D., Lohse, M. J., et al. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.735431 · Cited by 104
Self-citation
Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?
Evangelista, W., Ellingson, S. R., Smith, J. C., & Baudry, J. Y. (2019) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b11491 · Cited by 103
Setting the Record Straight: The Origin of the Pharmacophore Concept
Güner, O. F., & Bowen, J. P. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5000533 · Cited by 93
Pharmacophore Modeling Using Site-Identification by Ligand Competitive Saturation (SILCS) with Multiple Probe Molecules
Yu, W., Lakkaraju, S. K., Raman, E. P., Fang, L., & MacKerell, A. D. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500691p · Cited by 90
AutoDock Bias: improving binding mode prediction and virtual screening using known protein–ligand interactions
Arcon, J. P., Modenutti, C. P., Avendaño, D., Lopez, E. D., Defelipe, L. A., Ambrosio, F. A., Turjanski, A. G., Forli, S., & Marti, M. A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz152 · Cited by 81
Enhancing Drug Discovery Through In Silico Screening: Strategies to Increase True Positives Retrieval Rates
Kirchmair, J., Distinto, S., Schuster, D., Spitzer, G. M., Langer, T., & Wolber, G. (2008) · Current Medicinal Chemistry · doi:10.2174/092986708785132843 · Cited by 75
Self-citation
Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations
Wieder, M., Garon, A., Perricone, U., Boresch, S., Seidel, T., Almerico, A. M., & Langer, T. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00674 · Cited by 75
Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling
Yu, W., Lakkaraju, S. K., Raman, E. P., & MacKerell, A. D. (2014) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9728-0 · Cited by 72
LigVoxel: inpainting binding pockets using 3D-convolutional neural networks
Škalič, M., Varela‐Rial, A., Jiménez-Luna, J., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty583 · Cited by 67
Discovery of Potent Soluble Epoxide Hydrolase (sEH) Inhibitors by Pharmacophore-Based Virtual Screening
Waltenberger, B., Garscha, U., Temml, V., Liers, J., Werz, O., Schuster, D., & Stuppner, H. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00592 · Cited by 60
Identification of Bioactive Natural Products by Pharmacophore-Based Virtual Screening
Schuster, D., & Wolber, G. (2010) · Current Pharmaceutical Design · doi:10.2174/138161210791164072 · Cited by 57
Self-citation
Discovery of Novel Succinate Dehydrogenase Inhibitors by the Integration of in Silico Library Design and Pharmacophore Mapping
Yao, T., Fang, S., Li, Z.-S., Xiao, D.-X., Cheng, J., Ying, H., Du, Y.-J., Zhao, J., & Dong, X. (2017) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.7b00249 · Cited by 55
Structure-based design of N-substituted 1-hydroxy-4-sulfamoyl-2-naphthoates as selective inhibitors of the Mcl-1 oncoprotein
Lanning, M. E., Yu, W., Yap, J. L., Chauhan, J., Chen, L., Whiting, E., Pidugu, L. S. M., Atkinson, T., Bailey, H., Li, W., et al. (2016) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2016.02.006 · Cited by 54
Strategies for the discovery of biased GPCR ligands
Bermúdez, M., Nguyen, T. N., Omieczynski, C., & Wolber, G. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.02.010 · Cited by 54
Self-citation
Protein Pharmacophore Selection Using Hydration-Site Analysis
Hu, B., & Lill, M. A. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200620h · Cited by 53
Arginase Structure and Inhibition: Catalytic Site Plasticity Reveals New Modulation Possibilities
Mortier, J., Prévost, J. R. C., Sydow, D., Teuchert, S., Omieczynski, C., Bermúdez, M., Frédérick, R., & Wolber, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-13366-4 · Cited by 52
Self-citation
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Agnetta, L., Bermúdez, M., Riefolo, F., Matera, C., Claro, E., Messerer, R., Littmann, T., Wolber, G., Holzgrabe, U., & Decker, M. W. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01822 · Cited by 49
Self-citation
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Murgueitio, M. S., Rakers, C., Frank, A., & Wolber, G. (2016) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2016.10.007 · Cited by 42
Self-citation
Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations
Lee, J. Y., Krieger, J., Li, H., & Bahar, İ. (2019) · Protein Science · doi:10.1002/pro.3732 · Cited by 42
In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations
Rakers, C., Schumacher, F., Meinl, W., Glatt, H. R., Kleuser, B., & Wolber, G. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.685610 · Cited by 39
Self-citation
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Jung, S. W., Kim, M., Ramsey, S., Kurtzman, T., & Cho, A. E. (2018) · Scientific Reports · doi:10.1038/s41598-018-28546-z · Cited by 38
Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces
Mortier, J., Dhakal, P., & Volkamer, A. (2018) · Molecules · doi:10.3390/molecules23081959 · Cited by 38
The novel small-molecule antagonist MMG-11 preferentially inhibits TLR2/1 signaling
Grabowski, M., Murgueitio, M. S., Bermúdez, M., Wolber, G., & Weindl, G. (2019) · Biochemical Pharmacology · doi:10.1016/j.bcp.2019.113687 · Cited by 37
Self-citation
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Murgueitio, M. S., Henneke, P., Glossmann, H. H., Santos‐Sierra, S., & Wolber, G. (2014) · ChemMedChem · doi:10.1002/cmdc.201300445 · Cited by 36
Self-citation
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Durairaj, P., Fan, L., Machalz, D., Wolber, G., & Bureik, M. (2019) · FEBS Letters · doi:10.1002/1873-3468.13489 · Cited by 34
Self-citation
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Ntie‐Kang, F., Simoben, C. V., Karaman, B., Ngwa, V. F., Judson, P. N., Sippl, W., & Meva’à, L. M. (2016) · Drug Design Development and Therapy · doi:10.2147/dddt.s108118 · Cited by 33
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Self-citation
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See all 269

Authors: Schaller, D., Šribar, D., Noonan, T., Deng, L., Nguyen, T. N., Pach, S., Machalz, D., Bermudez, M., & Wolber, G.
Venue: WIREs Computational Molecular Science
Publisher: Wiley
Published: 2020-02-26
Volume: 10
Issue: 4
ISSN: 1759-0876 (ISSN-L)
1759-0884
DOI: 10.1002/wcms.1468
OpenAlex: W3006781192
Cited by: 269
References: 102
FWCI: 14.89
Type: journal article
Language: en
Open access: hybrid

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Next generation 3D pharmacophore modeling

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