Towards the Bioassay Activity Landscape Modeling in Compound Databases

Abstract

Public compound databases annotated with biological activity are increasingly being used in drug discovery programs. A prominent example is of such databases is PubChem. Herein, we introduce an approach to systematically characterize the structure-bioassay activity relationships in PubChem using the concept of <em>bioassay activity</em> landscape. This strategy is general and can be applied to any data set screened across multiple bioassays. We also present a visual representation of the chemical space of an in-house data set using a recently developed web-based public tool.

Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

“…This encoding of the activity data enabled the systematic structureand bioprole activity similarity and identied bioassay activity prole cliffs i.e., pairs of compounds with high structure similarity but very different bioassay activity proles. 37 In a separate work Yongye et al analyzed the ALM of a chemogenomics data set released by Clemons et al The data set contained more than 15 000 compounds from different sources (commercial compounds, natural products and synthetic molecules) that where screened across 100 sequence-unrelated proteins. 38 SMARt analysis using SAS maps led to the identication of structural changes that differentiated highly specic from promiscuous compounds.…”

Structural and Functional Diversities of the Hexadecahydro‐1H‐cyclopenta[a]phenanthrene Framework, a Ubiquitous Scaffold in Steroidal Hormones

“…Several analyses of the distribution of physicochemical properties of different NPs databases have been performed; for example, studies by Feher and Schmidt,32 Singh et al 33. and Medina Franco et al 34. A chemoinformatics analysis by Yongye and Medina‐Franco reported quantitative measures that can predict the changes in binding profiles of compounds as a function of their structural diversity 34…”

“…and Medina Franco et al 34. A chemoinformatics analysis by Yongye and Medina‐Franco reported quantitative measures that can predict the changes in binding profiles of compounds as a function of their structural diversity 34…”