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The 'SAR Matrix' method and its extensions for applications in medicinal chemistry and chemogenomics.

Gupta-Ostermann D, Bajorath J - F1000Res (2014)

Bottom Line: The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets.It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces.The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany.

ABSTRACT
We describe the 'Structure-Activity Relationship (SAR) Matrix' (SARM) methodology that is based upon a special two-step application of the matched molecular pair (MMP) formalism. The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets. It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces. The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.

No MeSH data available.


Candidate compound selection and activity prediction.In (a), a SARM is shown that represents a highly continuous local SAR environment. In this case, the potency of a virtual compound can be predicted using the NBH-based approach. By contrast, (b) shows a SARM representing a discontinuous local SAR. Activity cliff-forming compound pairs are highlighted in blue. Such SAR environments fall outside the applicability domain of NBH-based potency predictions. However, marked VCs represent promising candidates for compound design based on their proximity to activity cliffs. Both SARMs originate from a set of cannabinoid CB1 receptor ligands (compound structures are omitted for clarity).
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f6: Candidate compound selection and activity prediction.In (a), a SARM is shown that represents a highly continuous local SAR environment. In this case, the potency of a virtual compound can be predicted using the NBH-based approach. By contrast, (b) shows a SARM representing a discontinuous local SAR. Activity cliff-forming compound pairs are highlighted in blue. Such SAR environments fall outside the applicability domain of NBH-based potency predictions. However, marked VCs represent promising candidates for compound design based on their proximity to activity cliffs. Both SARMs originate from a set of cannabinoid CB1 receptor ligands (compound structures are omitted for clarity).

Mentions: Importantly, the NBH-based mini-QSAR approach is only applicable to candidate compounds falling into SARMs that represent continuous SAR regions, as illustrated inFigure 6a. By contrast, compounds falling into discontinuous SAR regions, as shown inFigure 6b, fall outside the applicability of standard QSAR modeling. Nonetheless, VCs from SARMs representing discontinuous SAR regions are also attractive candidates for compound design. This especially applies to VCs falling into the vicinity of activity cliffs10 that are formed by pairs of structural analogs with large potency differences, as illustrated inFigure 6b. Activity cliffs represent the pinnacle of SAR discontinuity. VCs in the vicinity of activity cliff can often be expected to display large (positive or negative) potency fluctuations and are hence attractive candidates in the search for potent hits. Although a QSAR formalism cannot be applied to predict the potency of such compounds, they can be easily selected from SARMs containing activity cliffs on the basis of a “guilt-by-association” principle, i.e., VCs are preferentially selected that are neighbors of potent activity cliff partners. For this purpose, SARMs capturing high degrees of local SAR discontinuity are selected on the basis of discontinuity ranking (vide supra).


The 'SAR Matrix' method and its extensions for applications in medicinal chemistry and chemogenomics.

Gupta-Ostermann D, Bajorath J - F1000Res (2014)

Candidate compound selection and activity prediction.In (a), a SARM is shown that represents a highly continuous local SAR environment. In this case, the potency of a virtual compound can be predicted using the NBH-based approach. By contrast, (b) shows a SARM representing a discontinuous local SAR. Activity cliff-forming compound pairs are highlighted in blue. Such SAR environments fall outside the applicability domain of NBH-based potency predictions. However, marked VCs represent promising candidates for compound design based on their proximity to activity cliffs. Both SARMs originate from a set of cannabinoid CB1 receptor ligands (compound structures are omitted for clarity).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4215758&req=5

f6: Candidate compound selection and activity prediction.In (a), a SARM is shown that represents a highly continuous local SAR environment. In this case, the potency of a virtual compound can be predicted using the NBH-based approach. By contrast, (b) shows a SARM representing a discontinuous local SAR. Activity cliff-forming compound pairs are highlighted in blue. Such SAR environments fall outside the applicability domain of NBH-based potency predictions. However, marked VCs represent promising candidates for compound design based on their proximity to activity cliffs. Both SARMs originate from a set of cannabinoid CB1 receptor ligands (compound structures are omitted for clarity).
Mentions: Importantly, the NBH-based mini-QSAR approach is only applicable to candidate compounds falling into SARMs that represent continuous SAR regions, as illustrated inFigure 6a. By contrast, compounds falling into discontinuous SAR regions, as shown inFigure 6b, fall outside the applicability of standard QSAR modeling. Nonetheless, VCs from SARMs representing discontinuous SAR regions are also attractive candidates for compound design. This especially applies to VCs falling into the vicinity of activity cliffs10 that are formed by pairs of structural analogs with large potency differences, as illustrated inFigure 6b. Activity cliffs represent the pinnacle of SAR discontinuity. VCs in the vicinity of activity cliff can often be expected to display large (positive or negative) potency fluctuations and are hence attractive candidates in the search for potent hits. Although a QSAR formalism cannot be applied to predict the potency of such compounds, they can be easily selected from SARMs containing activity cliffs on the basis of a “guilt-by-association” principle, i.e., VCs are preferentially selected that are neighbors of potent activity cliff partners. For this purpose, SARMs capturing high degrees of local SAR discontinuity are selected on the basis of discontinuity ranking (vide supra).

Bottom Line: The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets.It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces.The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany.

ABSTRACT
We describe the 'Structure-Activity Relationship (SAR) Matrix' (SARM) methodology that is based upon a special two-step application of the matched molecular pair (MMP) formalism. The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets. It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces. The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.

No MeSH data available.