ArticleJournal of cheminformatics2016
Improving chemical similarity ensemble approach in target prediction.
Article in Journal of cheminformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- CDMMM: a comprehensive platform of traditional Indian medicinal plant DNA barcodes and metabolite fingerprints database.Scientific reports · 2026Article
- Identifying key molecular interactions and pathways linking DEHP and its metabolite MEHP to allergic rhinitis: a network toxicology and molecular dynamics study.BMC pharmacology & toxicology · 2026Article
- Itaconate modulates myeloid inflammation in myocardial infarction via metabolic and structural reprogramming.Genes & genomics · 2026Article
- The identification of metabolites from gut microbiota in HPV infection via network pharmacology.PloS one · 2026Article
- Hyperoside Attenuates Lupus Nephritis-Associated Mesangial Cell Apoptosis via the P53/XAF1 Pathway: Integrative Bioinformatics and In Vitro Validation.Journal of inflammation research · 2026Article
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14 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drug-related studies. There are various models available analogous to SEA, because this approach is based on different types of molecular fingerprints. To investigate the influence of training data selection and the complementarity of different models, several SEA models were constructed and tested.
resultsWhen we used a test set of 37,138 positive and 42,928 negative ligand-target interactions, among the five tested molecular fingerprint methods, at significance level 0.05, Topological-based model yielded the best precision rate (83.7 %) and [Formula: see text] (0.784) while Atom pair-based model yielded the best [Formula: see text] (0.694). By employing an election system to combine the five models, a flexible prediction scheme was achieved with precision range from 71 to 90.6 %, [Formula: see text] range from 0.663 to 0.684 and [Formula: see text] range from 0.696 to 0.817.
conclusionsThe overall effectiveness of all of the five models could be ranked in decreasing order as follows: Atom pair [Formula: see text] Topological > Morgan > MACCS > Pharmacophore. Combining multiple SEA models, which takes advantages of different models, could be used to improve the success rates of the models. Another possibility of improving the model could be using target-specific classes or more active compounds.
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