ArticlePloS one2013
A linear combination of pharmacophore hypotheses as a new tool in search of new active compounds--an application for 5-HT1A receptor ligands.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.Pharmaceutics · 2021Article
- Three-dimensional descriptors for aminergic GPCRs: dependence on docking conformation and crystal structure.Molecular diversity · 2019Article
- In Silico Methods for the Discovery of Orthosteric GABAMolecules (Basel, Switzerland) · 2019Article
- Synthesis and anticonvulsant activity of phenoxyacetyl derivatives of amines, including aminoalkanols and amino acids.MedChemComm · 2018Article
- Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.Molecular diversity · 2017Article
- From Homology Models to a Set of Predictive Binding Pockets-a 5-HTJournal of chemical information and modeling · 2017Article
- Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores.Journal of cheminformatics · 2016Article
- An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.PloS one · 2016Article
- Bioisosteric matrices for ligands of serotonin receptors.ChemMedChem · 2015Article
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7 authors.
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Abstract
This study explores a new approach to pharmacophore screening involving the use of an optimized linear combination of models instead of a single hypothesis. The implementation and evaluation of the developed methodology are performed for a complete known chemical space of 5-HT1AR ligands (3616 active compounds with K i < 100 nM) acquired from the ChEMBL database. Clusters generated from three different methods were the basis for the individual pharmacophore hypotheses, which were assembled into optimal combinations to maximize the different coefficients, namely, MCC, accuracy and recall, to measure the screening performance. Various factors that influence filtering efficiency, including clustering methods, the composition of test sets (random, the most diverse and cluster population-dependent) and hit mode (the compound must fit at least one or two models from a final combination) were investigated. This method outmatched both single hypothesis and random linear combination approaches.
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