ArticleJournal of biomolecular screening2014
Automated Structure-Activity Relationship Mining: Connecting Chemical Structure to Biological Profiles.
Article in Journal of biomolecular screening, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Integrated Phytochemical and Pharmacological Insights intoPlants (Basel, Switzerland) · 2026Review
- The OASIS Consortium: integrating multi-omics technologies to transform chemical safety assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Phytochemical Profile and α-Amylase Inhibitory Activity of Pomegranate Peel Extract: An In Silico and In Vitro Investigation.Food science & nutrition · 2025Article
- Advanced In Vitro Models for Preclinical Drug Safety: Recent Progress and Prospects.Current issues in molecular biology · 2024Review
- Predicting compound activity from phenotypic profiles and chemical structures.Nature communications · 2023Article
- Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function.Journal of chemical information and modeling · 2022Review
- Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling.Toxicology and applied pharmacology · 2020Article
- DNA Barcoding a Complete Matrix of Stereoisomeric Small Molecules.Journal of the American Chemical Society · 2019Article
- Systematic exploration of cell morphological phenotypes associated with a transcriptomic query.Nucleic acids research · 2018Article
- Drug-induced adverse events prediction with the LINCS L1000 data.Bioinformatics (Oxford, England) · 2016Article
- Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset.Cancer discovery · 2015Article
- Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes.Cell · 2015Review
- Integrating phenotypic small-molecule profiling and human genetics: the next phase in drug discovery.Trends in genetics : TIG · 2015Review
- Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Understanding the structure-activity relationships (SARs) of small molecules is important for developing probes and novel therapeutic agents in chemical biology and drug discovery. Increasingly, multiplexed small-molecule profiling assays allow simultaneous measurement of many biological response parameters for the same compound (e.g., expression levels for many genes or binding constants against many proteins). Although such methods promise to capture SARs with high granularity, few computational methods are available to support SAR analyses of high-dimensional compound activity profiles. Many of these methods are not generally applicable or reduce the activity space to scalar summary statistics before establishing SARs. In this article, we present a versatile computational method that automatically extracts interpretable SAR rules from high-dimensional profiling data. The rules connect chemical structural features of compounds to patterns in their biological activity profiles. We applied our method to data from novel cell-based gene-expression and imaging assays collected on more than 30,000 small molecules. Based on the rules identified for this data set, we prioritized groups of compounds for further study, including a novel set of putative histone deacetylase inhibitors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.