Fabian Mayr, Christian Vieider, Veronika Temml, Hermann Stuppner, Daniela Schuster
{"title":"天然产品的开放式活性预测工具。案例研究:hERG 阻断剂。","authors":"Fabian Mayr, Christian Vieider, Veronika Temml, Hermann Stuppner, Daniela Schuster","doi":"10.1007/978-3-030-14632-0_6","DOIUrl":null,"url":null,"abstract":"<p><p>Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.</p>","PeriodicalId":50455,"journal":{"name":"The FASEB Journal","volume":"22 1","pages":"177-238"},"PeriodicalIF":4.4000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers.\",\"authors\":\"Fabian Mayr, Christian Vieider, Veronika Temml, Hermann Stuppner, Daniela Schuster\",\"doi\":\"10.1007/978-3-030-14632-0_6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.</p>\",\"PeriodicalId\":50455,\"journal\":{\"name\":\"The FASEB Journal\",\"volume\":\"22 1\",\"pages\":\"177-238\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The FASEB Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/978-3-030-14632-0_6\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The FASEB Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/978-3-030-14632-0_6","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Open-Access Activity Prediction Tools for Natural Products. Case Study: hERG Blockers.
Interference with the hERG potassium ion channel may cause cardiac arrhythmia and can even lead to death. Over the last few decades, several drugs, already on the market, and many more investigational drugs in various development stages, have had to be discontinued because of their hERG-associated toxicity. To recognize potential hERG activity in the early stages of drug development, a wide array of computational tools, based on different principles, such as 3D QSAR, 2D and 3D similarity, and machine learning, have been developed and are reviewed in this chapter. The various available prediction tools Similarity Ensemble Approach, SuperPred, SwissTargetPrediction, HitPick, admetSAR, PASSonline, Pred-hERG, and VirtualToxLab™ were used to screen a dataset of known hERG synthetic and natural product actives and inactives to quantify and compare their predictive power. This contribution will allow the reader to evaluate the suitability of these computational methods for their own related projects. There is an unmet need for natural product-specific prediction tools in this field.
期刊介绍:
The FASEB Journal publishes international, transdisciplinary research covering all fields of biology at every level of organization: atomic, molecular, cell, tissue, organ, organismic and population. While the journal strives to include research that cuts across the biological sciences, it also considers submissions that lie within one field, but may have implications for other fields as well. The journal seeks to publish basic and translational research, but also welcomes reports of pre-clinical and early clinical research. In addition to research, review, and hypothesis submissions, The FASEB Journal also seeks perspectives, commentaries, book reviews, and similar content related to the life sciences in its Up Front section.