ArticlePloS one2016
An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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
6 citing papers in PubMed.
- Establishing Detection Methods for Okadaic Acid Aptamer-Target Interactions: Insights from Computational and Experimental Approaches.Foods (Basel, Switzerland) · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes.Journal of chemical information and modeling · 2023Article
- Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.Pharmaceutics · 2021Article
- pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures.Bioinformatics advances · 2021Article
- Structural determinants influencing halogen bonding: a case study on azinesulfonamide analogs of aripiprazole as 5-HTChemistry Central journal · 2018Article
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Authors and funding
4 authors.
Funding
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Abstract
A computational procedure to search for selective ligands for structurally related protein targets was developed and verified for serotonergic 5-HT7/5-HT1A receptor ligands. Starting from a set of compounds with annotated activity at both targets (grouped into four classes according to their activity: selective toward each target, not-selective and not-selective but active) and with an additional set of decoys (prepared using DUD methodology), the SVM (Support Vector Machines) models were constructed using a selective subset as positive examples and four remaining classes as negative training examples. Based on these four component models, the consensus classifier was then constructed using a data fusion approach. The combination of two approaches of data representation (molecular fingerprints vs. structural interaction fingerprints), different training set sizes and selection of the best SVM component models for consensus model generation, were evaluated to determine the optimal settings for the developed algorithm. The results showed that consensus models with molecular fingerprints, a larger training set and the selection of component models based on MCC maximization provided the best predictive performance.
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