ArticleJournal of chemical information and modeling2011
Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.
Article in Journal of chemical information and modeling, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Data-driven frameworks to robustly predict solubility parameter of diverse polymers.Scientific reports · 2025Article
- ML-based Models as a Strategy to Discover Novel Antiepileptic Drugs Targeting Sodium Receptor Channel.Current topics in medicinal chemistry · 2025Article
- Be aware of overfitting by hyperparameter optimization!Journal of cheminformatics · 2024Article
- Understanding predictions of drug profiles using explainable machine learning models.BioData mining · 2024Article
- Machine learning for flow batteries: opportunities and challenges.Chemical science · 2022Review
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- A micro-XRT image analysis and machine learning methodology for the characterisation of multi-particulate capsule formulations.International journal of pharmaceutics: X · 2020Article
- ADME prediction with KNIME:ADMET & DMPK · 2020Article
- Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks.Molecules (Basel, Switzerland) · 2019Article
- Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity.Bioorganic & medicinal chemistry · 2019Article
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
- Article
- An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data.Analytica chimica acta · 2014Article
- Discovering associations in biomedical datasets by link-based associative classifier (LAC).PloS one · 2012Article
- Open Babel: An open chemical toolbox.Journal of cheminformatics · 2011Article
- Exploratory analysis of kinetic solubility measurements of a small molecule library.Bioorganic & medicinal chemistry · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.
Indexed as
Identifiers
What OpenQuestion holds
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.