{"title":"从化学结构预测活性/性质的进展。","authors":"Arianne Saunders, Peter de Boves Harrington","doi":"10.1080/10408347.2022.2066461","DOIUrl":null,"url":null,"abstract":"<p><p>Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.</p>","PeriodicalId":10744,"journal":{"name":"Critical reviews in analytical chemistry","volume":"1 1","pages":"135-147"},"PeriodicalIF":4.2000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advances in Activity/Property Prediction from Chemical Structures.\",\"authors\":\"Arianne Saunders, Peter de Boves Harrington\",\"doi\":\"10.1080/10408347.2022.2066461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.</p>\",\"PeriodicalId\":10744,\"journal\":{\"name\":\"Critical reviews in analytical chemistry\",\"volume\":\"1 1\",\"pages\":\"135-147\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Critical reviews in analytical chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1080/10408347.2022.2066461\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/4/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical reviews in analytical chemistry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1080/10408347.2022.2066461","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/4/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Advances in Activity/Property Prediction from Chemical Structures.
Recent technological advancement in AI modeling of molecular property databases has significantly expanded the opportunities for drug design and development. Quantitative structure-activity relationships (QSARs) are shown to provide more accurate predictions with regards to biological activity as well as toxicological assessment. By using a combination of in-silico models or by combining disparate structure-activity databases, researchers have been able to improve accuracy for a variety of drug discovery and analysis methods, generating viable compounds, which in certain cases, can be synthesized and further studied in vitro to find candidates for potential development. Additionally, the development of compounds of determined toxicology can be discontinued earlier, allowing alternative routes to be evaluated, preventing wasted time and resources. Although the progress that has been made is tremendous, expert review is still necessary for most in-silico generated predictions. Regardless, the scientific community continues to move ever closer to completely automated drug discovery and evaluation.
期刊介绍:
Critical Reviews in Analytical Chemistry continues to be a dependable resource for both the expert and the student by providing in-depth, scholarly, insightful reviews of important topics within the discipline of analytical chemistry and related measurement sciences. The journal exclusively publishes review articles that illuminate the underlying science, that evaluate the field''s status by putting recent developments into proper perspective and context, and that speculate on possible future developments. A limited number of articles are of a "tutorial" format written by experts for scientists seeking introduction or clarification in a new area.
This journal serves as a forum for linking various underlying components in broad and interdisciplinary means, while maintaining balance between applied and fundamental research. Topics we are interested in receiving reviews on are the following:
· chemical analysis;
· instrumentation;
· chemometrics;
· analytical biochemistry;
· medicinal analysis;
· forensics;
· environmental sciences;
· applied physics;
· and material science.