ArticleMolecules (Basel, Switzerland)2004
Application of 'inductive' QSAR descriptors for quantification of antibacterial activity of cationic polypeptides.
Article in Molecules (Basel, Switzerland), 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A scalable reinforcement learning approach for screening large peptide libraries for bioactive peptide discovery.Nature communications · 2025Article
- Improving the antimicrobial activity of RP9 peptide through theoretical and experimental investigation.Biochemistry and biophysics reports · 2025Article
- Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity.Amino acids · 2023Article
- Deep learning improves antimicrobial peptide recognition.Bioinformatics (Oxford, England) · 2018Article
- Peptides and Peptidomimetics for Antimicrobial Drug Design.Pharmaceuticals (Basel, Switzerland) · 2015Review
- Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine.PloS one · 2015Article
- QSAR modeling: where have you been? Where are you going to?Journal of medicinal chemistry · 2014Article
- Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes.Journal of chemical information and modeling · 2014Article
- Class IIa bacteriocins: diversity and new developments.International journal of molecular sciences · 2012Review
- Connecting peptide physicochemical and antimicrobial properties by a rational prediction model.PloS one · 2011Article
- Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample.International journal of molecular sciences · 2011Article
- Distinguishing compounds with anticancer activity by ANN using inductive QSAR descriptors.Bioinformation · 2008Article
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2 authors.
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Abstract
On the basis of the inductive QSAR descriptors we have created a neural network-based solution enabling quantification of antibacterial activity in the series of 101 synthetic cationic polypeptides (CAMEL-s). The developed QSAR model allowed 80% correct categorical classification of antibacterial potencies of the CAMEL-s both in the training and the validation sets. The accuracy of the activity predictions demonstrates that a narrow set of 3D sensitive 'inductive' descriptors can adequately describe the aspects of intra- and intermolecular interactions that are relevant for antibacterial activity of the cationic polypeptides. The developed approach can be further expanded for the larger sets of biologically active peptides and can serve as a useful quantitative tool for rational antibiotic design and discovery.
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