{"title":"基于人工智能的药物发现中的分子优化综合评述","authors":"Yuhang Xia, Yongkang Wang, Zhiwei Wang, Wen Zhang","doi":"10.1002/qub2.30","DOIUrl":null,"url":null,"abstract":"Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence‐based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping‐based, molecular distribution matching‐based, and guided search‐based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence‐based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive review of molecular optimization in artificial intelligence‐based drug discovery\",\"authors\":\"Yuhang Xia, Yongkang Wang, Zhiwei Wang, Wen Zhang\",\"doi\":\"10.1002/qub2.30\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence‐based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping‐based, molecular distribution matching‐based, and guided search‐based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence‐based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.\",\"PeriodicalId\":45660,\"journal\":{\"name\":\"Quantitative Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/qub2.30\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/qub2.30","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
A comprehensive review of molecular optimization in artificial intelligence‐based drug discovery
Drug discovery is aimed to design novel molecules with specific chemical properties for the treatment of targeting diseases. Generally, molecular optimization is one important step in drug discovery, which optimizes the physical and chemical properties of a molecule. Currently, artificial intelligence techniques have shown excellent success in drug discovery, which has emerged as a new strategy to address the challenges of drug design including molecular optimization, and drastically reduce the costs and time for drug discovery. We review the latest advances of molecular optimization in artificial intelligence‐based drug discovery, including data resources, molecular properties, optimization methodologies, and assessment criteria for molecular optimization. Specifically, we classify the optimization methodologies into molecular mapping‐based, molecular distribution matching‐based, and guided search‐based methods, respectively, and discuss the principles of these methods as well as their pros and cons. Moreover, we highlight the current challenges in molecular optimization and offer a variety of perspectives, including interpretability, multidimensional optimization, and model generalization, on potential new lines of research to pursue in future. This study provides a comprehensive review of molecular optimization in artificial intelligence‐based drug discovery, which points out the challenges as well as the new prospects. This review will guide researchers who are interested in artificial intelligence molecular optimization.
期刊介绍:
Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and gain a deeper understanding of the life sciences. It aims to provide a platform for not only the analysis but also the integration and construction of biological systems. It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers in order to promote rapid communication and exchange between scientists in the East and the West. The content of Quantitative Biology will mainly focus on the two broad and related areas: ·bioinformatics and computational biology, which focuses on dealing with information technologies and computational methodologies that can efficiently and accurately manipulate –omics data and transform molecular information into biological knowledge. ·systems and synthetic biology, which focuses on complex interactions in biological systems and the emergent functional properties, and on the design and construction of new biological functions and systems. Its goal is to reflect the significant advances made in quantitatively investigating and modeling both natural and engineered life systems at the molecular and higher levels. The journal particularly encourages original papers that link novel theory with cutting-edge experiments, especially in the newly emerging and multi-disciplinary areas of research. The journal also welcomes high-quality reviews and perspective articles.