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Naveja JJ, Vogt M. Automatic Identification of Analogue Series from Large Compound Data Sets: Methods and Applications. Molecules 2021; 26:5291. [PMID: 34500724 PMCID: PMC8433811 DOI: 10.3390/molecules26175291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 08/27/2021] [Accepted: 08/28/2021] [Indexed: 01/21/2023] Open
Abstract
Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis-Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments.
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Affiliation(s)
- José J. Naveja
- Instituto de Química, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5-6, 53115 Bonn, Germany
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2
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Fast Modeling of Binding Affinities by Means of Superposing Significant Interaction Rules (SSIR) Method. Int J Mol Sci 2016; 17:ijms17060827. [PMID: 27240346 PMCID: PMC4926361 DOI: 10.3390/ijms17060827] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 05/13/2016] [Accepted: 05/20/2016] [Indexed: 11/17/2022] Open
Abstract
The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.
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3
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Hu Y, Zhang B, Vogt M, Bajorath J. AnalogExplorer2 - Stereochemistry sensitive graphical analysis of large analog series. F1000Res 2015; 4:Chem Inf Sci-1031. [PMID: 26913194 PMCID: PMC4743145 DOI: 10.12688/f1000research.7146.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/01/2015] [Indexed: 11/20/2022] Open
Abstract
AnalogExplorer is a computational methodology for the extraction and organization of series of structural analogs from compound data sets and their graphical analysis. The method is suitable for the analysis of large analog series originating from lead optimization programs. Herein we report AnalogExplorer2 designed to explicitly take stereochemical information during graphical analysis into account and describe a freely available deposition of the original AnalogExplorer program, AnalogExplorer2, and exemplary compound sets to illustrate their use.
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
| | - Bijun Zhang
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany
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4
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Zhang B, Hu Y, Bajorath J. AnalogExplorer: A New Method for Graphical Analysis of Analog Series and Associated Structure–Activity Relationship Information. J Med Chem 2014; 57:9184-94. [DOI: 10.1021/jm501391g] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Bijun Zhang
- 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
| | - 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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5
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Kuyoc-Carrillo VF, Medina-Franco JL. Progress in the Analysis of Multiple Activity Profile of Screening Data Using Computational Approaches. Drug Dev Res 2014; 75:313-23. [DOI: 10.1002/ddr.21209] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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6
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de Souza A, Bittker JA, Lahr DL, Brudz S, Chatwin S, Oprea TI, Waller A, Yang JJ, Southall N, Guha R, Schürer SC, Vempati UD, Southern MR, Dawson ES, Clemons PA, Chung TDY. An Overview of the Challenges in Designing, Integrating, and Delivering BARD: A Public Chemical-Biology Resource and Query Portal for Multiple Organizations, Locations, and Disciplines. ACTA ACUST UNITED AC 2014; 19:614-27. [PMID: 24441647 DOI: 10.1177/1087057113517139] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2013] [Accepted: 11/22/2013] [Indexed: 01/15/2023]
Abstract
Recent industry-academic partnerships involve collaboration among disciplines, locations, and organizations using publicly funded "open-access" and proprietary commercial data sources. These require the effective integration of chemical and biological information from diverse data sources, which presents key informatics, personnel, and organizational challenges. The BioAssay Research Database (BARD) was conceived to address these challenges and serve as a community-wide resource and intuitive web portal for public-sector chemical-biology data. Its initial focus is to enable scientists to more effectively use the National Institutes of Health Roadmap Molecular Libraries Program (MLP) data generated from the 3-year pilot and 6-year production phases of the Molecular Libraries Probe Production Centers Network (MLPCN), which is currently in its final year. BARD evolves the current data standards through structured assay and result annotations that leverage BioAssay Ontology and other industry-standard ontologies, and a core hierarchy of assay definition terms and data standards defined specifically for small-molecule assay data. We initially focused on migrating the highest-value MLP data into BARD and bringing it up to this new standard. We review the technical and organizational challenges overcome by the interdisciplinary BARD team, veterans of public- and private-sector data-integration projects, who are collaborating to describe (functional specifications), design (technical specifications), and implement this next-generation software solution.
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Affiliation(s)
| | | | - David L Lahr
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Steve Brudz
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Simon Chatwin
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Tudor I Oprea
- University of New Mexico Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Anna Waller
- University of New Mexico Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Jeremy J Yang
- University of New Mexico Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Noel Southall
- NIH Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Rajarshi Guha
- NIH Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Stephan C Schürer
- Center for Computational Science, University of Miami, Miami, FL, USA
| | - Uma D Vempati
- Center for Computational Science, University of Miami, Miami, FL, USA
| | - Mark R Southern
- The Translational Research Institute, The Scripps Research Institute, Jupiter, FL, USA
| | - Eric S Dawson
- The Vanderbilt Specialized Chemistry Center for Accelerated Probe Development, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Thomas D Y Chung
- Conrad Prebys Center for Chemical Genomics, Sanford
- Burnham Medical Research Institute, La Jolla, CA, USA
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7
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Stumpfe D, Hu Y, Dimova D, Bajorath J. Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem 2013; 57:18-28. [PMID: 23981118 DOI: 10.1021/jm401120g] [Citation(s) in RCA: 151] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The activity cliff concept is of high relevance for medicinal chemistry. Recent studies are discussed that have further refined our understanding of activity cliffs and suggested different ways of exploiting activity cliff information. These include alternative approaches to define and classify activity cliffs in two and three dimensions, data mining investigations to systematically detect all possible activity cliffs, the introduction of computational methods to predict activity cliffs, and studies designed to explore activity cliff progression in medicinal chemistry. The discussion of these studies is complemented with new findings revealing the frequency of activity cliff formation when different molecular representations are used and the distribution of activity cliffs across different targets. Taken together, the results have a number of implications for the practice of medicinal chemistry.
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Affiliation(s)
- 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
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8
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Yongye AB, Medina-Franco JL. Toward an Efficient Approach to Identify Molecular Scaffolds Possessing Selective or Promiscuous Compounds. Chem Biol Drug Des 2013; 82:367-75. [DOI: 10.1111/cbdd.12162] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Revised: 04/12/2013] [Accepted: 04/17/2013] [Indexed: 01/09/2023]
Affiliation(s)
- Austin B. Yongye
- Torrey Pines Institute for Molecular Studies; 11350 SW Village Parkway Port St. Lucie FL 34987 USA
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies; 11350 SW Village Parkway Port St. Lucie FL 34987 USA
- Instituto de Química; Universidad Nacional Autónoma de México; Circuito Exterior; Ciudad Universitaria; México D.F 04510 Mexico
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9
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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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10
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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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11
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Abstract
Understanding structure-activity relationships (SARs) for a given set of molecules allows one to rationally explore chemical space and develop a chemical series optimizing multiple physicochemical and biological properties simultaneously, for instance, improving potency, reducing toxicity, and ensuring sufficient bioavailability. In silico methods allow rapid and efficient characterization of SARs and facilitate building a variety of models to capture and encode one or more SARs, which can then be used to predict activities for new molecules. By coupling these methods with in silico modifications of structures, one can easily prioritize large screening decks or even generate new compounds de novo and ascertain whether they belong to the SAR being studied. Computational methods can provide a guide for the experienced user by integrating and summarizing large amounts of preexisting data to suggest useful structural modifications. This chapter highlights the different types of SAR modeling methods and how they support the task of exploring chemical space to elucidate and optimize SARs in a drug discovery setting. In addition to considering modeling algorithms, I briefly discuss how to use databases as a source of SAR data to inform and enhance the exploration of SAR trends. I also review common modeling techniques that are used to encode SARs, recent work in the area of structure-activity landscapes, the role of SAR databases, and alternative approaches to exploring SAR data that do not involve explicit model development.
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Affiliation(s)
- Rajarshi Guha
- NIH Center for Advancing Translational Science, Rockville, MD, USA
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12
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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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13
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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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14
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Guha R. Exploring Structure-Activity Data Using the Landscape Paradigm. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2012; 2. [PMID: 24163705 DOI: 10.1002/wcms.1087] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In this article we present an overview of the origin and applications of the activity landscape view of structure-actvitiy relationship data as conceived by Maggiora. Within this landscape, different regions exemplify different aspects of SAR trends - ranging from smoothly varying trends to discontinuous trends (also termed activity cliffs). We discuss the various definitions of landscapes and cliffs that have been proposed as well as different approaches to the numerical quantification of a landscape. We then highlight some of the landscape visualization approaches that have been developed, followed by a review of the various applications of activity landscapes and cliffs to topics in medicinal chemistry and SAR analysis.
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Affiliation(s)
- Rajarshi Guha
- NIH Center for Translational Therapeutics 9800 Medical Center Drive Rockville, MD 20850
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15
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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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16
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Wassermann AM, Bajorath J. Directed R-group combination graph: a methodology to uncover structure-activity relationship patterns in a series of analogues. J Med Chem 2012; 55:1215-26. [PMID: 22248436 DOI: 10.1021/jm201362h] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A graphical method is introduced to study details of structure-activity relationships (SARs) in analogue series that further extends conventional analysis of analogues using R-group tables or related approaches and that provides additional and more differentiated SAR information. The newly designed graph structure represents entire series of analogues in a consistent manner, regardless of their size and complexity of substitution patterns. The approach is specifically tailored toward a systematic exploration and intuitive interpretation of SAR features involving different R-groups and their combinations. Analogues and their potency information are systematically organized on the basis of R-group combinations that are present in a series. This organization scheme results in graph components that represent well-defined SAR patterns. Analysis of these patterns provides an immediate access to critical substitution sites and R-group combinations, favorable and unfavorable R-groups, or nonadditive potency effects of multisite substitutions. Furthermore, the data structure makes it possible to design new analogues by combining favorable R-group combinations derived from different compounds.
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Affiliation(s)
- Anne Mai Wassermann
- 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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17
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Affiliation(s)
- 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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18
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Hack MD, Rassokhin DN, Buyck C, Seierstad M, Skalkin A, ten Holte P, Jones TK, Mirzadegan T, Agrafiotis DK. Library Enhancement through the Wisdom of Crowds. J Chem Inf Model 2011; 51:3275-86. [DOI: 10.1021/ci200446y] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Michael D. Hack
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., 3210 Merryfield Row, San Diego, California 92121, United States
| | - Dmitrii N. Rassokhin
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Christophe Buyck
- Janssen Research & Development, Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Mark Seierstad
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., 3210 Merryfield Row, San Diego, California 92121, United States
| | - Andrew Skalkin
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Peter ten Holte
- Janssen Research & Development, Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Todd K. Jones
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., 3210 Merryfield Row, San Diego, California 92121, United States
- Todd Jones Consulting, San Diego, California
| | - Taraneh Mirzadegan
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., 3210 Merryfield Row, San Diego, California 92121, United States
| | - Dimitris K. Agrafiotis
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
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19
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Agrafiotis DK, Lobanov VS, Shemanarev M, Rassokhin DN, Izrailev S, Jaeger EP, Alex S, Farnum M. Efficient Substructure Searching of Large Chemical Libraries: The ABCD Chemical Cartridge. J Chem Inf Model 2011; 51:3113-30. [DOI: 10.1021/ci200413e] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Dimitris K. Agrafiotis
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Victor S. Lobanov
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Maxim Shemanarev
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Dmitrii N. Rassokhin
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Sergei Izrailev
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Edward P. Jaeger
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Simson Alex
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
| | - Michael Farnum
- Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
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20
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Yongye AB, Byler K, Santos R, Martínez-Mayorga K, Maggiora GM, Medina-Franco JL. Consensus Models of Activity Landscapes with Multiple Chemical, Conformer, and Property Representations. J Chem Inf Model 2011; 51:1259-70. [DOI: 10.1021/ci200081k] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Austin B. Yongye
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Kendall Byler
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Radleigh Santos
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Karina Martínez-Mayorga
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
| | - Gerald M. Maggiora
- Department of Pharmacology & Toxicology, University of Arizona College of Pharmacy, 1703 E. Mabel Street, Tucson, Arizona 85721, United States and Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, Arizona 85004, United States
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port St. Lucie, Florida 34987, United States
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