scholarly journals Collection of analog series-based scaffolds from public compound sources

2018 ◽  
Vol 4 (4) ◽  
pp. FSO287 ◽  
Author(s):  
Dilyana Dimova ◽  
Jürgen Bajorath
Keyword(s):  
Analog Series

scholarly journals DeepCOMO: from structure-activity relationship diagnostics to generative molecular design using the compound optimization monitor methodology

2020 ◽  
Vol 34 (12) ◽  
pp. 1207-1218
Author(s):  
Dimitar Yonchev ◽  
Jürgen Bajorath
Keyword(s):  
Molecular Design ◽  
Structure Activity Relationship ◽  
Development Stage ◽  
Activity Relationship ◽  
Analog Design ◽  
Design Strategies ◽  
Structure Activity ◽  
Scientific Context ◽  
Analog Series ◽  
Relationship Progression

Abstract The compound optimization monitor (COMO) approach was originally developed as a diagnostic approach to aid in evaluating development stages of analog series and progress made during lead optimization. COMO uses virtual analog populations for the assessment of chemical saturation of analog series and has been further developed to bridge between optimization diagnostics and compound design. Herein, we discuss key methodological features of COMO in its scientific context and present a deep learning extension of COMO for generative molecular design, leading to the introduction of DeepCOMO. Applications on exemplary analog series are reported to illustrate the entire DeepCOMO repertoire, ranging from chemical saturation and structure–activity relationship progression diagnostics to the evaluation of different analog design strategies and prioritization of virtual candidates for optimization efforts, taking into account the development stage of individual analog series.

scholarly journals A general approach for retrosynthetic molecular core analysis

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
J. Jesús Naveja ◽  
B. Angélica Pilón-Jiménez ◽  
Jürgen Bajorath ◽  
José L. Medina-Franco
Keyword(s):  
Single Molecule ◽  
Chemical Space ◽  
Significant Proportion ◽  
Bipartite Network ◽  
Data Sets ◽  
Structure Property ◽  
Data Set ◽  
Chemical Structures ◽  
Structure Property Relationships ◽  
Analog Series

Abstract Scaffold analysis of compound data sets has reemerged as a chemically interpretable alternative to machine learning for chemical space and structure–activity relationships analysis. In this context, analog series-based scaffolds (ASBS) are synthetically relevant core structures that represent individual series of analogs. As an extension to ASBS, we herein introduce the development of a general conceptual framework that considers all putative cores of molecules in a compound data set, thus softening the often applied “single molecule–single scaffold” correspondence. A putative core is here defined as any substructure of a molecule complying with two basic rules: (a) the size of the core is a significant proportion of the whole molecule size and (b) the substructure can be reached from the original molecule through a succession of retrosynthesis rules. Thereafter, a bipartite network consisting of molecules and cores can be constructed for a database of chemical structures. Compounds linked to the same cores are considered analogs. We present case studies illustrating the potential of the general framework. The applications range from inter- and intra-core diversity analysis of compound data sets, structure–property relationships, and identification of analog series and ASBS. The molecule–core network herein presented is a general methodology with multiple applications in scaffold analysis. New statistical methods are envisioned that will be able to draw quantitative conclusions from these data. The code to use the method presented in this work is freely available as an additional file. Follow-up applications include analog searching and core structure–property relationships analyses.

scholarly journals Activity profiles of analog series containing pan assay interference compounds

RSC Advances ◽  
2017 ◽  
Vol 7 (57) ◽  
pp. 35638-35647 ◽  
Author(s):  
Erik Gilberg ◽  
Dagmar Stumpfe ◽  
Jürgen Bajorath
Keyword(s):  
Analog Series ◽  
Assay Interference

Shown is the distribution of activity profiles (color-coded bars) of analog series containing PAINS substructures in a heatmap.

scholarly journals AnalogExplorer2 – Stereochemistry sensitive graphical analysis of large analog series

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1031 ◽  
Author(s):  
Ye Hu ◽  
Bijun Zhang ◽  
Martin Vogt ◽  
Jürgen Bajorath
Keyword(s):  
Graphical Analysis ◽  
Lead Optimization ◽  
Data Sets ◽  
Analog Series ◽  
Computational Methodology

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.

scholarly journals Systematic analysis of structural and activity relationships between conventional hierarchical and analog series-based scaffolds

RSC Advances ◽  
2017 ◽  
Vol 7 (30) ◽  
pp. 18718-18723 ◽  
Author(s):  
Dagmar Stumpfe ◽  
Dilyana Dimova ◽  
Jürgen Bajorath
Keyword(s):  
Biological Activity ◽  
Systematic Analysis ◽  
Analog Series ◽  
Hierarchical Scaffold

Three pairs of compounds (left) belonging to three different analog series that differ in their biological activity share a single conventional hierarchical scaffold (BM, middle) but have distinct ‘analog series-based’ (ASB) scaffold (right).

scholarly journals Computational method for estimating progression saturation of analog series

RSC Advances ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 5484-5492 ◽  
Author(s):  
Ryo Kunimoto ◽  
Tomoyuki Miyao ◽  
Jürgen Bajorath
Keyword(s):  
Chemical Space ◽  
Computational Method ◽  
Analog Series

Chemical space view of an analog series. Shown are inactive (red) and active (blue) analogs together with virtual candidate compounds (green) available to expand the series. Chemical neighborhoods of analogs are depicted in gray.

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