{"title":"Syn-MolOpt:使用数据衍生功能反应模板的合成规划驱动分子优化方法","authors":"Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou","doi":"10.1186/s13321-025-00975-9","DOIUrl":null,"url":null,"abstract":"<div><p>Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt’s robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.</p><p><b>Scientific contribution</b> Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt’s universal workflow makes it suitable for various types of molecular optimization tasks.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1000,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00975-9","citationCount":"0","resultStr":"{\"title\":\"Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates\",\"authors\":\"Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou\",\"doi\":\"10.1186/s13321-025-00975-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt’s robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.</p><p><b>Scientific contribution</b> Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt’s universal workflow makes it suitable for various types of molecular optimization tasks.</p></div>\",\"PeriodicalId\":617,\"journal\":{\"name\":\"Journal of Cheminformatics\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2025-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00975-9\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Cheminformatics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1186/s13321-025-00975-9\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cheminformatics","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1186/s13321-025-00975-9","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates
Molecular optimization is a crucial step in drug development, involving structural modifications to improve the desired properties of drug candidates. Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. To address this issue, we first developed a general pipeline capable of constructing functional reaction template library specific to any property where a predictive model can be built. Based on these functional templates, we introduced Syn-MolOpt, a synthesis planning-oriented molecular optimization method. During optimization, functional reaction templates steer the process towards specific properties by effectively transforming relevant structural fragments. In four diverse tasks, including two toxicity-related (GSK3β-Mutagenicity and GSK3β-hERG) and two metabolism-related (GSK3β-CYP3A4 and GSK3β-CYP2C19) multi-property molecular optimizations, Syn-MolOpt outperformed three benchmark models (Modof, HierG2G, and SynNet), highlighting its efficacy and adaptability. Additionally, visualization of the synthetic routes for molecules optimized by Syn-MolOpt confirms the effectiveness of functional reaction templates in molecular optimization. Notably, Syn-MolOpt’s robust performance in scenarios with limited scoring accuracy demonstrates its potential for real-world molecular optimization applications. By considering both optimization and synthesizability, Syn-MolOpt promises to be a valuable tool in molecular optimization.
Scientific contribution Syn-MolOpt takes into account both molecular optimization and synthesis, allowing for the design of property-specific functional reaction template libraries for the properties to be optimized, and providing reference synthesis routes for the optimized compounds while optimizing the targeted properties. Syn-MolOpt’s universal workflow makes it suitable for various types of molecular optimization tasks.
期刊介绍:
Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling.
Coverage includes, but is not limited to:
chemical information systems, software and databases, and molecular modelling,
chemical structure representations and their use in structure, substructure, and similarity searching of chemical substance and chemical reaction databases,
computer and molecular graphics, computer-aided molecular design, expert systems, QSAR, and data mining techniques.