A machine learning framework for predicting peptide inhibitors of insect voltage-gated sodium channels.
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| Title: | A machine learning framework for predicting peptide inhibitors of insect voltage-gated sodium channels. |
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| Authors: | da Silva Sousa J; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil; Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest Bahia, Jequié, BA, Brazil., Palmeira LS; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil; Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest Bahia, Jequié, BA, Brazil., Barbosa FS; Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest Bahia, Jequié, BA, Brazil., Mercado HMP; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil., Melo TS; Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest Bahia, Jequié, BA, Brazil., Azevedo V; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil., Góes-Neto A; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil; Department of Microbiology, Molecular and Computational Biology of Fungi Laboratory, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil., Xavier JS; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil; Computer Science Division, Technological Institute of Aeronautics, SP, São José dos Campos, 89760-000, Brazil; Center for Epidemic Response and Innovation (CERI), School of Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa., Andrade BS; Graduate Program in Bioinformatics, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil; Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest Bahia, Jequié, BA, Brazil. Electronic address: bandrade@uesb.edu.br. |
| Source: | Journal of molecular graphics & modelling [J Mol Graph Model] 2026 Jul; Vol. 146, pp. 109398. Date of Electronic Publication: 2026 Apr 08. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Science, Inc Country of Publication: United States NLM ID: 9716237 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-4243 (Electronic) Linking ISSN: 10933263 NLM ISO Abbreviation: J Mol Graph Model Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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