{"title":"药物发现中的概念密度泛函理论综述。","authors":"Hemangini Rohit, Hiteshi Tandon","doi":"10.1007/s00894-025-06487-5","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>As a development from Density Functional Theory, Conceptual Density Functional Theory (CDFT) has emerged as a valuable complementary approach in modern drug discovery. Both global and local chemical reactivity descriptors within the framework of CDFT have made it easier to study chemical reactions and how drugs affect their targets. They aid to predict the electronic properties of drug candidates, which simplifies the process of enhancing important characteristics such as their binding affinity, level of selectivity among others. They help in exploring and analyzing inhibitors that work specifically and allow to predict the negative effects those inhibitors may have. When applied with other approaches such as molecular docking and QSAR modeling, CDFT strengthens the whole drug discovery process. This review highlights the increasing importance of CDFT in rational drug design and especially in context of combined efforts with molecular docking and QSAR modeling. It also provides a fundamental knowledge of CDFT-based descriptors and their role in drug discovery process. In addition to highlighting existing challenges, this review outlines potential future directions.</p></div>","PeriodicalId":651,"journal":{"name":"Journal of Molecular Modeling","volume":"31 11","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Conceptual density functional theory in drug discovery: an overview\",\"authors\":\"Hemangini Rohit, Hiteshi Tandon\",\"doi\":\"10.1007/s00894-025-06487-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Context</h3><p>As a development from Density Functional Theory, Conceptual Density Functional Theory (CDFT) has emerged as a valuable complementary approach in modern drug discovery. Both global and local chemical reactivity descriptors within the framework of CDFT have made it easier to study chemical reactions and how drugs affect their targets. They aid to predict the electronic properties of drug candidates, which simplifies the process of enhancing important characteristics such as their binding affinity, level of selectivity among others. They help in exploring and analyzing inhibitors that work specifically and allow to predict the negative effects those inhibitors may have. When applied with other approaches such as molecular docking and QSAR modeling, CDFT strengthens the whole drug discovery process. This review highlights the increasing importance of CDFT in rational drug design and especially in context of combined efforts with molecular docking and QSAR modeling. It also provides a fundamental knowledge of CDFT-based descriptors and their role in drug discovery process. In addition to highlighting existing challenges, this review outlines potential future directions.</p></div>\",\"PeriodicalId\":651,\"journal\":{\"name\":\"Journal of Molecular Modeling\",\"volume\":\"31 11\",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Molecular Modeling\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s00894-025-06487-5\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Molecular Modeling","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s00894-025-06487-5","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Conceptual density functional theory in drug discovery: an overview
Context
As a development from Density Functional Theory, Conceptual Density Functional Theory (CDFT) has emerged as a valuable complementary approach in modern drug discovery. Both global and local chemical reactivity descriptors within the framework of CDFT have made it easier to study chemical reactions and how drugs affect their targets. They aid to predict the electronic properties of drug candidates, which simplifies the process of enhancing important characteristics such as their binding affinity, level of selectivity among others. They help in exploring and analyzing inhibitors that work specifically and allow to predict the negative effects those inhibitors may have. When applied with other approaches such as molecular docking and QSAR modeling, CDFT strengthens the whole drug discovery process. This review highlights the increasing importance of CDFT in rational drug design and especially in context of combined efforts with molecular docking and QSAR modeling. It also provides a fundamental knowledge of CDFT-based descriptors and their role in drug discovery process. In addition to highlighting existing challenges, this review outlines potential future directions.
期刊介绍:
The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling.
Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry.
Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.