Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic identification of target set-dependent activity cliffs.

ABSTRACT: Aim:Generating a knowledge base of new activity cliffs (ACs) defined on the basis of compound set-dependent potency distributions, also taking confirmed inactive compounds into account. Methodology:Different AC definitions, representations and search criteria were rationalized and applied. Data:For nearly 100 different target proteins, for which medicinal chemistry and biological screening data were available, target set-dependent ACs were identified. More than 20,000 target set-dependent ACs and associated information are made freely available. Limitations & next steps:As more compound data become available for new targets, the search for target set-dependent ACs, including confirmed inactive compounds will continue. Second-generation ACs will be subjected to systematic structure-activity relationship analysis.

SUBMITTER: Hu H

PROVIDER: S-EPMC6391634 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Systematic identification of target set-dependent activity cliffs.

Hu Huabin H Stumpfe Dagmar D Bajorath Jürgen J

Future science OA 20190118 2

<h4>Aim</h4>Generating a knowledge base of new activity cliffs (ACs) defined on the basis of compound set-dependent potency distributions, also taking confirmed inactive compounds into account.<h4>Methodology</h4>Different AC definitions, representations and search criteria were rationalized and applied.<h4>Data</h4>For nearly 100 different target proteins, for which medicinal chemistry and biological screening data were available, target set-dependent ACs were identified. More than 20,000 targe ...[more]

PMID: 30828462

Similar Datasets

Computational method for the identification of third generation activity cliffs.

Project description:In medicinal chemistry and chemoinformatics, activity cliffs (ACs) are defined as pairs of structurally similar compounds that are active against the same target but have a large difference in potency. Accordingly, ACs are rich in structure-activity relationship (SAR) information, which rationalizes their relevance for medicinal chemistry. For identifying ACs, a compound similarity criterion and a potency difference criterion must be specified. So far a constant potency difference between AC partner compounds has mostly been set, e.g. 100-fold, irrespective of the specific activity (targets) of cliff-forming compounds. Herein, we introduce a computational methodology for AC identification and analysis that includes three novel components: •ACs are identified on the basis of variable target set-dependent potency difference criteria (a 'target set' represents a collection of compounds that are active against a given target protein).•ACs are extracted from computationally determined analog series (ASs) and consist of pairs of analogs with single or multiple substitution sites.•For multi-site ACs, a search for analogs with individual substitutions is performed to analyze their contributions to AC formation and determine if multi-site ACs can be represented by single-site ACs.

| S-EPMC6974786 | biostudies-literature

Structure-based predictions of activity cliffs.

Project description:In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods.

| S-EPMC4553268 | biostudies-literature

Systematic identification of 3'UTR regulatory elements in activity-dependent mRNA stability in hippocampal neurons

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2014-03-12 | E-GEOD-55781 | biostudies-arrayexpress

Exposing the Limitations of Molecular Machine Learning with Activity Cliffs.

Project description:Machine learning has become a crucial tool in drug discovery and chemistry at large, e.g., to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure but exhibit large differences in potency─have received limited attention for their effect on model performance. Not only are these edge cases informative for molecule discovery and optimization but also models that are well equipped to accurately predict the potency of activity cliffs have increased potential for prospective applications. Our work aims to fill the current knowledge gap on best-practice machine learning methods in the presence of activity cliffs. We benchmarked a total of 24 machine and deep learning approaches on curated bioactivity data from 30 macromolecular targets for their performance on activity cliff compounds. While all methods struggled in the presence of activity cliffs, machine learning approaches based on molecular descriptors outperformed more complex deep learning methods. Our findings highlight large case-by-case differences in performance, advocating for (a) the inclusion of dedicated "activity-cliff-centered" metrics during model development and evaluation and (b) the development of novel algorithms to better predict the properties of activity cliffs. To this end, the methods, metrics, and results of this study have been encapsulated into an open-access benchmarking platform named MoleculeACE (Activity Cliff Estimation, available on GitHub at: https://github.com/molML/MoleculeACE). MoleculeACE is designed to steer the community toward addressing the pressing but overlooked limitation of molecular machine learning models posed by activity cliffs.

| S-EPMC9749029 | biostudies-literature

Increasing the public activity cliff knowledge base with new categories of activity cliffs.

Project description:AimExtending the public knowledge base of activity cliffs (ACs) with new categories of ACs having special structural characteristics.MethodologyDual-site ACs, isomer ACs and ACs with privileged substructures are described and their systematic identification is detailed.Exemplary results & dataMore than 7400 new ACs belonging to different categories with activity against more than 200 targets were identified and are made publicly available.Limitations & next stepsFor dual-site ACs, limited numbers of isomers are available as structural analogs for rationalizing contributions to AC formation. The search for such analogs will continue. In addition, the target distribution of ACs containing privileged substructures will be further analyzed.

| S-EPMC7273365 | biostudies-literature

On the validity versus utility of activity landscapes: are all activity cliffs statistically significant?

Project description:BackgroundMost work on the topic of activity landscapes has focused on their quantitative description and visual representation, with the aim of aiding navigation of SAR. Recent developments have addressed applications such as quantifying the proportion of activity cliffs, investigating the predictive abilities of activity landscape methods and so on. However, all these publications have worked under the assumption that the activity landscape models are "real" (i.e., statistically significant).ResultsThe current study addresses for the first time, in a quantitative manner, the significance of a landscape or individual cliffs in the landscape. In particular, we question whether the activity landscape derived from observed (experimental) activity data is different from a randomly generated landscape. To address this we used the SALI measure with six different data sets tested against one or more molecular targets. We also assessed the significance of the landscapes for single and multiple representations.ConclusionsWe find that non-random landscapes are data set and molecular representation dependent. For the data sets and representations used in this work, our results suggest that not all representations lead to non-random landscapes. This indicates that not all molecular representations should be used to a) interpret the SAR and b) combined to generate consensus models. Our results suggest that significance testing of activity landscape models and in particular, activity cliffs, is key, prior to the use of such models.

| S-EPMC4021161 | biostudies-literature

Systematic identification of 3'UTR regulatory elements in activity-dependent mRNA stability in hippocampal neurons

Project description:This SuperSeries is composed of the SubSeries listed below.

2014-03-12 | GSE55781 | GEO

Systematic identification of human SNPs affecting regulatory element activity

Project description:Most of the millions of single-nucleotide polymorphisms (SNPs) in the human genome are non-coding, and many overlap with putative regulatory elements. Genome-wide association studies have linked many of these SNPs to human traits or to gene expression levels, but rarely with sufficient resolution to identify the causal SNPs. Functional screens based on reporter assays have previously been of insufficient throughput to test the vast space of SNPs for possible effects on enhancer and promoter activity. Here, we have leveraged the throughput of the SuRE reporter technology to survey a total of 5.9 million SNPs, including 57% of the known common SNPs world-wide. We identified more than 30 thousand SNPs that alter the activity of putative regulatory elements, often in a cell-type specific manner. These data indicate that a large proportion of human non-coding SNPs may affect gene regulation. Integration of these SuRE data with genome-wide association studies may help to identify causal SNPs.

2019-04-05 | GSE128325 | GEO

Identification of a set of KSRP target transcripts upregulated by PI3K-AKT signaling.

Project description:BACKGROUND:KSRP is a AU-rich element (ARE) binding protein that causes decay of select sets of transcripts in different cell types. We have recently described that phosphatidylinositol 3-kinase/AKT (PI3K-AKT) activation induces stabilization and accumulation of the labile beta-catenin mRNA through an impairment of KSRP function. RESULTS:Aim of this study was to identify additional KSRP targets whose stability and steady-state levels are enhanced by PI3K-AKT activation. First, through microarray analyses of the AU-rich transcriptome in pituitary alphaT3-1 cells, we identified 34 ARE-containing transcripts upregulated in cells expressing a constitutively active form of AKT1. In parallel, by an affinity chromatography-based technique followed by microarray analyses, 12 mRNAs target of KSRP, additional to beta-catenin, were identified. Among them, seven mRNAs were upregulated in cells expressing activated AKT1. Both steady-state levels and stability of these new KSRP targets were consistently increased by either KSRP knock-down or PI3K-AKT activation. CONCLUSION:Our study identified a set of transcripts that are targets of KSRP and whose expression is increased by PI3K-AKT activation. These mRNAs encode RNA binding proteins, signaling molecules and a replication-independent histone. The increased expression of these gene products upon PI3K-AKT activation could play a role in the cellular events initiated by this signaling pathway.

| S-EPMC1858702 | biostudies-literature

Target Explorer: An automated tool for the identification of new target genes for a specified set of transcription factors.

Project description:With the increasing number of eukaryotic genomes available, high-throughput automated tools for identification of regulatory DNA sequences are becoming increasingly feasible. Several computational approaches for the prediction of regulatory elements were recently developed. Here we combine the prediction of clusters of binding sites for transcription factors with context information taken from genome annotations. Target Explorer automates the entire process from the creation of a customized library of binding sites for known transcription factors through the prediction and annotation of putative target genes that are potentially regulated by these factors. It was specifically designed for the well-annotated Drosophila melanogaster genome, but most options can be used for sequences from other genomes as well. Target Explorer is available at http://trantor.bioc.columbia.edu/Target_Explorer/

| S-EPMC168951 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data