201
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Dimova D, Stumpfe D, Bajorath J. Quantifying the Fingerprint Descriptor Dependence of Structure–Activity Relationship Information on a Large Scale. J Chem Inf Model 2013; 53:2275-81. [DOI: 10.1021/ci4004078] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Dilyana Dimova
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany
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202
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Automated molecule editing in molecular design. J Comput Aided Mol Des 2013; 27:655-64. [DOI: 10.1007/s10822-013-9676-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2013] [Accepted: 08/23/2013] [Indexed: 12/20/2022]
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203
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Abstract
The analysis of structure–activity relationships (SARs) is a central task in medicinal chemistry. Traditionally, SAR exploration has concentrated on individual compound series. This conventional approach is complemented by large-scale SAR analysis, which puts strong emphasis on data mining and SAR visualization. This contribution reviews recent concepts for large-scale SAR analysis including numerical functions to characterize global and local SAR information content of compound data sets, alternative activity landscape representations and data mining strategies.
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204
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Jin F, Gao D, Wu Q, Liu F, Chen Y, Tan C, Jiang Y. Exploration of N-(2-aminoethyl)piperidine-4-carboxamide as a potential scaffold for development of VEGFR-2, ERK-2 and Abl-1 multikinase inhibitor. Bioorg Med Chem 2013; 21:5694-706. [DOI: 10.1016/j.bmc.2013.07.026] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2013] [Revised: 07/11/2013] [Accepted: 07/11/2013] [Indexed: 01/09/2023]
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205
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Hu Y, Stumpfe D, Bajorath J. Visualization of Activity Landscapes and Chemogenomics Data. Mol Inform 2013; 32:954-63. [DOI: 10.1002/minf.201300044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Accepted: 06/11/2013] [Indexed: 01/23/2023]
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206
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Vogt M, Iyer P, Maggiora GM, Bajorath J. Conditional Probabilities of Activity Landscape Features for Individual Compounds. J Chem Inf Model 2013; 53:1602-12. [DOI: 10.1021/ci400288r] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Martin Vogt
- Department of Life
Science Informatics, B-IT, LIMES Program Unit Chemical
Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Preeti Iyer
- Department of Life
Science Informatics, B-IT, LIMES Program Unit Chemical
Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Gerald M. Maggiora
- College of Pharmacy & BIO5 Institute, University of Arizona, Translational Genomics Research Institute, 1295 North Martin, P.O. Box 210202, Tucson, Arizona 85721, United States, and 445 North Fifth Street, Phoenix, Arizona 85004, United States
| | - Jürgen Bajorath
- Department of Life
Science Informatics, B-IT, LIMES Program Unit Chemical
Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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207
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Zhu T, Cao S, Su PC, Patel R, Shah D, Chokshi HB, Szukala R, Johnson ME, Hevener KE. Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis. J Med Chem 2013; 56:6560-72. [PMID: 23688234 DOI: 10.1021/jm301916b] [Citation(s) in RCA: 174] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
A critical analysis of virtual screening results published between 2007 and 2011 was performed. The activity of reported hit compounds from over 400 studies was compared to their hit identification criteria. Hit rates and ligand efficiencies were calculated to assist in these analyses, and the results were compared with factors such as the size of the virtual library and the number of compounds tested. A series of promiscuity, druglike, and ADMET filters were applied to the reported hits to assess the quality of compounds reported, and a careful analysis of a subset of the studies that presented hit optimization was performed. These data allowed us to make several practical recommendations with respect to selection of compounds for experimental testing, definition of hit identification criteria, and general virtual screening hit criteria to allow for realistic hit optimization. A key recommendation is the use of size-targeted ligand efficiency values as hit identification criteria.
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Affiliation(s)
- Tian Zhu
- Center for Pharmaceutical Biotechnology, University of Illinois at Chicago , 900 S. Ashland Avenue, Suite 3100, Chicago, Illinois 60607-7173, United States
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208
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Medina-Franco JL, Edwards BS, Pinilla C, Appel JR, Giulianotti MA, Santos RG, Yongye AB, Sklar LA, Houghten RA. Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches. J Chem Inf Model 2013; 53:1475-85. [PMID: 23705689 DOI: 10.1021/ci400192y] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries.
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Affiliation(s)
- José L Medina-Franco
- Torrey Pines Institute for Molecular Studies, Port St. Lucie, Florida 34987, USA.
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209
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Medina-Franco JL. Activity Cliffs: Facts or Artifacts? Chem Biol Drug Des 2013; 81:553-6. [DOI: 10.1111/cbdd.12115] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Revised: 01/17/2013] [Accepted: 01/27/2013] [Indexed: 01/12/2023]
Affiliation(s)
- José L. Medina-Franco
- Instituto de Química, Universidad Nacional Autónoma de México, Circuito Exterior; Ciudad Universitaria; México; D.F. 04510; Mexico
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210
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Stumpfe D, Dimova D, Heikamp K, Bajorath J. Compound Pathway Model To Capture SAR Progression: Comparison of Activity Cliff-Dependent and -Independent Pathways. J Chem Inf Model 2013; 53:1067-72. [DOI: 10.1021/ci400141w] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Dagmar Stumpfe
- Department
of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Dilyana Dimova
- Department
of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Kathrin Heikamp
- Department
of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department
of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
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211
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Dimova D, Heikamp K, Stumpfe D, Bajorath J. Do Medicinal Chemists Learn from Activity Cliffs? A Systematic Evaluation of Cliff Progression in Evolving Compound Data Sets. J Med Chem 2013; 56:3339-45. [DOI: 10.1021/jm400147j] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Dilyana Dimova
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Kathrin Heikamp
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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212
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Estrada AA, Shore DG, Blackwood E, Chen YH, Deshmukh G, Ding X, DiPasquale AG, Epler JA, Friedman LS, Koehler MFT, Liu L, Malek S, Nonomiya J, Ortwine DF, Pei Z, Sideris S, St-Jean F, Trinh L, Truong T, Lyssikatos JP. Pyrimidoaminotropanes as Potent, Selective, and Efficacious Small Molecule Kinase Inhibitors of the Mammalian Target of Rapamycin (mTOR). J Med Chem 2013; 56:3090-101. [DOI: 10.1021/jm400194n] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Affiliation(s)
| | | | | | | | | | | | - Antonio G. DiPasquale
- X-ray Crystallographic Facility, University of California—Berkeley, 32 Lewis
Hall, Berkeley, California 94720, United States
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213
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Wassermann AM, Lounkine E, Glick M. Bioturbo similarity searching: combining chemical and biological similarity to discover structurally diverse bioactive molecules. J Chem Inf Model 2013; 53:692-703. [PMID: 23461561 DOI: 10.1021/ci300607r] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.
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Affiliation(s)
- Anne Mai Wassermann
- In Silico Lead Discovery, Novartis Institutes for Biomedical Research Inc. , 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
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214
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Hu Y, Bajorath J. Introduction of Target Cliffs as a Concept To Identify and Describe Complex Molecular Selectivity Patterns. J Chem Inf Model 2013; 53:545-52. [DOI: 10.1021/ci300602m] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
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215
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Iyer P, Stumpfe D, Vogt M, Bajorath J, Maggiora GM. Activity Landscapes, Information Theory, and Structure - Activity Relationships. Mol Inform 2013; 32:421-30. [DOI: 10.1002/minf.201200120] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2012] [Accepted: 12/13/2012] [Indexed: 12/16/2022]
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216
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Petrone PM, Wassermann AM, Lounkine E, Kutchukian P, Simms B, Jenkins J, Selzer P, Glick M. Biodiversity of small molecules--a new perspective in screening set selection. Drug Discov Today 2013; 18:674-80. [PMID: 23454345 DOI: 10.1016/j.drudis.2013.02.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2012] [Revised: 01/28/2013] [Accepted: 02/07/2013] [Indexed: 11/27/2022]
Abstract
How is the 'diversity' of a compound set defined and how is the most appropriate compound subset identified for assay when screening the entire HTS deck is not an option? A common approach has so far been to cover as much of the chemical space as possible by screening a chemically diverse set of compounds. We show that, rather than chemical diversity, the biologic diversity of a compound library is an essential requirement for hit identification. We describe a simple and efficient approach for the design of a HTS library based on compound-target diversity. Biodiverse compound subsets outperform chemically diverse libraries regarding hit rate and the total number of unique chemical scaffolds present among hits. Specifically, by screening ~19% of a HTS collection, we expect to discover ~50-80% of all desired bioactive compounds.
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Affiliation(s)
- Paula M Petrone
- Cheminformatics and Statistics, Hoffmann-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland
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217
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Pérez-Villanueva J, Méndez-Lucio O, Soria-Arteche O, Izquierdo T, Concepción Lozada M, Gloria-Greimel WA, Medina-Franco JL. Cyclic Systems Distribution Along Similarity Measures: Insights for an Application to Activity Landscape Modeling. Mol Inform 2013; 32:179-90. [DOI: 10.1002/minf.201200127] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2012] [Accepted: 12/21/2012] [Indexed: 12/12/2022]
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218
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Activity cliffs in PubChem confirmatory bioassays taking inactive compounds into account. J Comput Aided Mol Des 2013; 27:115-24. [DOI: 10.1007/s10822-012-9632-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2012] [Accepted: 12/29/2012] [Indexed: 11/27/2022]
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219
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de Graaf C, Vischer HF, de Kloe GE, Kooistra AJ, Nijmeijer S, Kuijer M, Verheij MHP, England PJ, van Muijlwijk-Koezen JE, Leurs R, de Esch IJP. Small and colorful stones make beautiful mosaics: fragment-based chemogenomics. Drug Discov Today 2012; 18:323-30. [PMID: 23266367 DOI: 10.1016/j.drudis.2012.12.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2012] [Revised: 11/19/2012] [Accepted: 12/05/2012] [Indexed: 12/01/2022]
Abstract
Smaller stones with a wide variety of colors make a higher resolution mosaic. In much the same way, smaller chemical entities that are structurally diverse are better able to interrogate protein binding sites. This feature article describes the construction of a diverse fragment library and an analysis of the screening of six representative protein targets belonging to three diverse target classes (G protein-coupled receptors ADRB2, H1R, H3R, and H4R, the ligand-gated ion channel 5-HT3R, and the kinase PKA) using chemogenomics approaches. The integration of experimentally determined bioaffinity profiles across related and unrelated protein targets and chemogenomics analysis of fragment binding and protein structure allow the identification of: (i) unexpected similarities and differences in ligand binding properties, and (ii) subtle ligand affinity and selectivity cliffs. With a wealth of fragment screening data being generated in industry and academia, such approaches will contribute to a more detailed structural understanding of ligand-protein interactions.
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Affiliation(s)
- Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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220
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Wijtmans M, Scholten DJ, Roumen L, Canals M, Custers H, Glas M, Vreeker MCA, de Kanter FJJ, de Graaf C, Smit MJ, de Esch IJP, Leurs R. Chemical Subtleties in Small-Molecule Modulation of Peptide Receptor Function: The Case of CXCR3 Biaryl-Type Ligands. J Med Chem 2012; 55:10572-83. [DOI: 10.1021/jm301240t] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Maikel Wijtmans
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Danny J. Scholten
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Luc Roumen
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Meritxell Canals
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Hans Custers
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Marjolein Glas
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Marlies C. A. Vreeker
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Frans J. J. de Kanter
- Division of Organic and Inorganic
Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Chris de Graaf
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Martine J. Smit
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Iwan J. P. de Esch
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
| | - Rob Leurs
- Leiden/Amsterdam Center for
Drug Research, Division of Medicinal Chemistry, Faculty of Sciences, VU University Amsterdam, The Netherlands
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221
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Méndez-Lucio O, Pérez-Villanueva J, Castillo R, Medina-Franco JL. Identifying Activity Cliff Generators of PPAR Ligands Using SAS Maps. Mol Inform 2012; 31:837-46. [DOI: 10.1002/minf.201200078] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2012] [Accepted: 10/06/2012] [Indexed: 01/27/2023]
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222
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Wassermann AM, Dimova D, Iyer P, Bajorath J. Advances in Computational Medicinal Chemistry: Matched Molecular Pair Analysis. Drug Dev Res 2012. [DOI: 10.1002/ddr.21045] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Anne Mai Wassermann
- Department of Life Science Informatics; B-IT; LIMES Program Unit Chemical Biology and Medicinal Chemistry; Rheinische Friedrich-Wilhelms-Universität Bonn; Dahlmannstr. 2; D-53113; Bonn; Germany
| | - Dilyana Dimova
- Department of Life Science Informatics; B-IT; LIMES Program Unit Chemical Biology and Medicinal Chemistry; Rheinische Friedrich-Wilhelms-Universität Bonn; Dahlmannstr. 2; D-53113; Bonn; Germany
| | - Preeti Iyer
- Department of Life Science Informatics; B-IT; LIMES Program Unit Chemical Biology and Medicinal Chemistry; Rheinische Friedrich-Wilhelms-Universität Bonn; Dahlmannstr. 2; D-53113; Bonn; Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics; B-IT; LIMES Program Unit Chemical Biology and Medicinal Chemistry; Rheinische Friedrich-Wilhelms-Universität Bonn; Dahlmannstr. 2; D-53113; Bonn; Germany
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223
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Affiliation(s)
- Michael Bieler
- Boehringer Ingelheim Pharma GmbH & Co. KG; Lead Discovery and Optimization Support; 88397; Biberach/Riss; Germany
| | - Herbert Koeppen
- Boehringer Ingelheim Pharma GmbH & Co. KG; Lead Discovery and Optimization Support; 88397; Biberach/Riss; Germany
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224
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Estrada AA, Liu X, Baker-Glenn C, Beresford A, Burdick DJ, Chambers M, Chan BK, Chen H, Ding X, DiPasquale AG, Dominguez SL, Dotson J, Drummond J, Flagella M, Flynn S, Fuji R, Gill A, Gunzner-Toste J, Harris SF, Heffron TP, Kleinheinz T, Lee DW, Le Pichon CE, Lyssikatos JP, Medhurst AD, Moffat JG, Mukund S, Nash K, Scearce-Levie K, Sheng Z, Shore DG, Tran T, Trivedi N, Wang S, Zhang S, Zhang X, Zhao G, Zhu H, Sweeney ZK. Discovery of highly potent, selective, and brain-penetrable leucine-rich repeat kinase 2 (LRRK2) small molecule inhibitors. J Med Chem 2012; 55:9416-33. [PMID: 22985112 DOI: 10.1021/jm301020q] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
There is a high demand for potent, selective, and brain-penetrant small molecule inhibitors of leucine-rich repeat kinase 2 (LRRK2) to test whether inhibition of LRRK2 kinase activity is a potentially viable treatment option for Parkinson's disease patients. Herein we disclose the use of property and structure-based drug design for the optimization of highly ligand efficient aminopyrimidine lead compounds. High throughput in vivo rodent cassette pharmacokinetic studies enabled rapid validation of in vitro-in vivo correlations. Guided by this data, optimal design parameters were established. Effective incorporation of these guidelines into our molecular design process resulted in the discovery of small molecule inhibitors such as GNE-7915 (18) and 19, which possess an ideal balance of LRRK2 cellular potency, broad kinase selectivity, metabolic stability, and brain penetration across multiple species. Advancement of GNE-7915 into rodent and higher species toxicity studies enabled risk assessment for early development.
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Affiliation(s)
- Anthony A Estrada
- Department of Discovery Chemistry, Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, USA.
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225
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Medina-Franco JL. Scanning structure-activity relationships with structure-activity similarity and related maps: from consensus activity cliffs to selectivity switches. J Chem Inf Model 2012; 52:2485-93. [PMID: 22989212 DOI: 10.1021/ci300362x] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Systematic description of structure-activity relationships (SARs) of data sets and structure-property relationships (SPRs) is of paramount importance in medicinal chemistry and other research fields. To this end, structure-activity similarity (SAS) maps are one of the first tools proposed to describe SARs using the concept of activity landscape modeling. One of the major goals of the SAS maps is to identify activity cliffs defined as chemical compounds with high similar structure but unexpectedly very different biological activity. Since the first publication of the SAS maps more than ten years ago, these tools have evolved and adapted over the years to analyze various types of compound collections, including structural diverse and combinatorial sets with activity for one or multiple biological end points. The development of SAS maps has led to general concepts that are applicable to other activity landscape methods such as "consensus activity cliffs" (activity cliffs common to a series of representations or descriptors) and "selectivity switches" (structural changes that completely invert the selectivity pattern of similar compounds against two biological end points). Herein, we review the development, practical applications, limitations, and perspectives of the SAS and related maps which are intuitive and powerful informatics tools to computationally analyze SPRs.
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Affiliation(s)
- José L Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, USA.
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226
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Gupta-Ostermann D, Bajorath J. Identification of Multitarget Activity Ridges in High-Dimensional Bioactivity Spaces. J Chem Inf Model 2012; 52:2579-86. [DOI: 10.1021/ci3003683] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Disha Gupta-Ostermann
- Department of Life Science
Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
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227
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Graphs and networks in chemical and biological informatics: past, present and future. Future Med Chem 2012; 4:2039-47. [DOI: 10.4155/fmc.12.128] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Chemical and biological network analysis has recently garnered intense interest from the perspective of drug design and discovery. While graph theoretic concepts have a long history in chemistry – predating quantum mechanics – and graphical measures of chemical structures date back to the 1970s, it is only recently with the advent of public repositories of information and availability of high-throughput assays and computational resources that network analysis of large-scale chemical networks, such as protein–protein interaction networks, has become possible. Drug design and discovery are undergoing a paradigm shift, from the notion of ‘one target, one drug’ to a much more nuanced view that relies on multiple sources of information: genomic, proteomic, metabolomic and so on. This holistic view of drug design is an incredibly daunting undertaking still very much in its infancy. Here, we focus on current developments in graph- and network-centric approaches in chemical and biological informatics, with particular reference to applications in the fields of SAR modeling and drug design. Key insights from the past suggest a path forward via visualization and fusion of multiple sources of chemical network data.
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228
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Heikamp K, Hu X, Yan A, Bajorath J. Prediction of Activity Cliffs Using Support Vector Machines. J Chem Inf Model 2012; 52:2354-65. [DOI: 10.1021/ci300306a] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Kathrin Heikamp
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
| | - Xiaoying Hu
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
- State
Key Laboratory of Chemical
Resource Engineering, Department of Pharmaceutical Engineering, P.O.
Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East
Road, Beijing 100029, People’s Republic of China
| | - Aixia Yan
- State
Key Laboratory of Chemical
Resource Engineering, Department of Pharmaceutical Engineering, P.O.
Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East
Road, Beijing 100029, People’s Republic of China
| | - Jürgen Bajorath
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
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229
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Yongye AB, Medina-Franco JL. Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds. J Chem Inf Model 2012; 52:2454-61. [PMID: 22856455 DOI: 10.1021/ci3002606] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Activity profiling of compound collections across multiple targets is increasingly being used in probe and drug discovery. Herein, we discuss an approach to systematically analyzing the structure-activity relationships of a large screening profile data with emphasis on identifying structural changes that have a significant impact on the number of proteins to which a compound binds. As a case study, we analyzed a recently released public data set of more than 15 000 compounds screened across 100 sequence-unrelated proteins. The screened compounds have different origins and include natural products, synthetic molecules from academic groups, and commercial compounds. Similar synthetic structures from academic groups showed, overall, greater promiscuity differences than do natural products and commercial compounds. The method implemented in this work readily identified structural changes that differentiated highly specific from promiscuous compounds. This approach is general and can be applied to analyze any other large-scale protein-binding profile data.
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Affiliation(s)
- Austin B Yongye
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, USA
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230
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Stumpfe D, Bajorath J. Frequency of Occurrence and Potency Range Distribution of Activity Cliffs in Bioactive Compounds. J Chem Inf Model 2012; 52:2348-53. [DOI: 10.1021/ci300288f] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Dagmar Stumpfe
- Department of Life Science
Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
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231
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Petrone PM, Simms B, Nigsch F, Lounkine E, Kutchukian P, Cornett A, Deng Z, Davies JW, Jenkins JL, Glick M. Rethinking molecular similarity: comparing compounds on the basis of biological activity. ACS Chem Biol 2012; 7:1399-409. [PMID: 22594495 DOI: 10.1021/cb3001028] [Citation(s) in RCA: 109] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor "high-throughput screening fingerprint" (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ~1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in the sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are valuable not only because of their predictive power but mainly because they relate compounds solely on the basis of bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility toward the understanding of how compounds interact with the proteome.
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Affiliation(s)
- Paula M. Petrone
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Benjamin Simms
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Florian Nigsch
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Eugen Lounkine
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Peter Kutchukian
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Allen Cornett
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Zhan Deng
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - John W. Davies
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Jeremy L. Jenkins
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Meir Glick
- Center for Proteomic Chemistry, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
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232
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Iyer P, Dimova D, Vogt M, Bajorath J. Navigating High-Dimensional Activity Landscapes: Design and Application of the Ligand-Target Differentiation Map. J Chem Inf Model 2012; 52:1962-9. [DOI: 10.1021/ci3002765] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Preeti Iyer
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
| | - Dilyana Dimova
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
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233
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Hu Y, Bajorath J. Extending the Activity Cliff Concept: Structural Categorization of Activity Cliffs and Systematic Identification of Different Types of Cliffs in the ChEMBL Database. J Chem Inf Model 2012; 52:1806-11. [DOI: 10.1021/ci300274c] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische
Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn,
Germany
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234
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Hu Y, Furtmann N, Gütschow M, Bajorath J. Systematic Identification and Classification of Three-Dimensional Activity Cliffs. J Chem Inf Model 2012; 52:1490-8. [DOI: 10.1021/ci300158v] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Ye Hu
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Norbert Furtmann
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
- Pharmaceutical Institute, University of Bonn, An der Immenburg 4, D-53121 Bonn,
Germany
| | - Michael Gütschow
- Pharmaceutical Institute, University of Bonn, An der Immenburg 4, D-53121 Bonn,
Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
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235
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Hu X, Hu Y, Vogt M, Stumpfe D, Bajorath J. MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs. J Chem Inf Model 2012; 52:1138-45. [DOI: 10.1021/ci3001138] [Citation(s) in RCA: 166] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Xiaoying Hu
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
- State Key Laboratory of Chemical
Resource Engineering, Department of Pharmaceutical Engineering, P.O.
Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East
Road, Beijing 100029, People’s Republic of China
| | - Ye Hu
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
| | - Martin Vogt
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry,
Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2,
D-53113 Bonn, Germany
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236
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Namasivayam V, Bajorath J. Searching for Coordinated Activity Cliffs Using Particle Swarm Optimization. J Chem Inf Model 2012; 52:927-34. [DOI: 10.1021/ci3000503] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Vigneshwaran Namasivayam
- Department of Life Science
Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr.
2, D-53113 Bonn, Germany
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237
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Hu Y, Bajorath J. Exploration of 3D Activity Cliffs on the Basis of Compound Binding Modes and Comparison of 2D and 3D Cliffs. J Chem Inf Model 2012; 52:670-7. [DOI: 10.1021/ci300033e] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse
2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES
Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse
2, D-53113 Bonn, Germany
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