{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T20:58:54Z","timestamp":1775163534743,"version":"3.50.1"},"reference-count":193,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7\u201335 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew\u2019s correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.<\/jats:p>","DOI":"10.3390\/ph15030323","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T01:53:54Z","timestamp":1646790834000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity"],"prefix":"10.3390","volume":"15","author":[{"given":"Alberto A.","family":"Robles-Loaiza","sequence":"first","affiliation":[{"name":"Biomolecules Discovery Group, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150150, Ecuador"}]},{"given":"Edgar A.","family":"Pinos-Tamayo","sequence":"additional","affiliation":[{"name":"Escuela Superior Polit\u00e9cnica del Litoral, ESPOL, Centro Nacional de Acuicultura e Investigaciones Marinas (CENAIM), Campus Gustavo Galindo Km. 30, 5 V\u00eda Perimetral, Guayaquil 09-01-5863, Ecuador"}]},{"given":"Bruno","family":"Mendes","sequence":"additional","affiliation":[{"name":"Biomolecules Discovery Group, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150150, Ecuador"}]},{"given":"Josselyn A.","family":"Ortega-Pila","sequence":"additional","affiliation":[{"name":"Biomolecules Discovery Group, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150150, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9279-1038","authenticated-orcid":false,"given":"Carolina","family":"Proa\u00f1o-Bola\u00f1os","sequence":"additional","affiliation":[{"name":"Biomolecules Discovery Group, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150150, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2246-9347","authenticated-orcid":false,"given":"Fabien","family":"Plisson","sequence":"additional","affiliation":[{"name":"Consejo Nacional de Ciencia y Tecnolog\u00eda, Unidad de Gen\u00f3mica Avanzada, Laboratorio Nacional de Gen\u00f3mica para la Biodiversidad (Langebio), Centro de Investigaci\u00f3n Y de Estudios Avanzados del IPN, Irapuato 36824, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9506-3781","authenticated-orcid":false,"given":"C\u00e1tia","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio Associado para a Qu\u00edmica Verde-REQUIMTE, Departamento de Qu\u00edmica e Bioqu\u00edmica, Faculdade de Ci\u00eancias, Universidade do Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6018-4724","authenticated-orcid":false,"given":"Paula","family":"Gomes","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio Associado para a Qu\u00edmica Verde-REQUIMTE, Departamento de Qu\u00edmica e Bioqu\u00edmica, Faculdade de Ci\u00eancias, Universidade do Porto, 4169-007 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4637-4468","authenticated-orcid":false,"given":"Jos\u00e9 R.","family":"Almeida","sequence":"additional","affiliation":[{"name":"Biomolecules Discovery Group, Universidad Regional Amaz\u00f3nica Ikiam, Tena 150150, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/s41573-020-00135-8","article-title":"Trends in peptide drug discovery","volume":"20","author":"Muttenthaler","year":"2021","journal-title":"Nat. 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