Haiqing Sun , Xuecong Tian , Zhuman Wen , Sizhe Zhang , Yaxuan Yang , Yixian Tu , Xiaoyi Lv
{"title":"GCNFG-DTA:Screening natural medicinal components of Cyperus esculentus targeting kinases with AIDD methods","authors":"Haiqing Sun , Xuecong Tian , Zhuman Wen , Sizhe Zhang , Yaxuan Yang , Yixian Tu , Xiaoyi Lv","doi":"10.1016/j.chemolab.2025.105317","DOIUrl":null,"url":null,"abstract":"<div><div>Screening bioactive molecules from natural plant compounds is currently a common approach in the field of drug discovery. <em>Cyperus esculentus</em>, a multipurpose crop primarily used for food, is highly valued in certain countries or regions for its unique medicinal properties. Although there is a foundational understanding of its components and pharmacological effects, exploration of its effective targets, especially kinase targets, remains insufficient. Our study integrates Artificial Intelligence-Assisted Drug Design (AIDD) by utilizing the KIBA and BindingDB datasets to train the GCNFG-DTA deep learning model for predicting the kinase target affinity of 152 active compounds from <em>Cyperus esculentus</em>. By screening for high-affinity molecule-kinase target pairs and employing molecular docking and molecular dynamics simulations, the study successfully identified pairs of the most promising active molecule-target combinations. Our predicting results demonstrate that the GCN-GAT-FG model, with its excellent predictive ability (Achieving a low MSE of 0.131 and a high CI of 0.896), significantly accelerates the discovery process of bioactive molecules. Further molecular docking validated that 15 high-affinity molecule-kinase target pairs had docking energy scores below −5 kJ/mol. Among these, 14 pairs exhibited stable conformations during 100 ns molecular dynamics simulations. Notably, Cyanidin chloride, N-Feruloyltyramine, and Imbricatonol were identified as the most promising molecules, demonstrating the high conformational stability when targeting the MAP3K8, CLK4 and FGR kinase targets, respectively. These findings provide a scientific basis for further exploring the medicinal potential of <em>Cyperus esculentus</em>. Overall, the deep learning method used in our study offers new insights into the field of drug discovery related to natural compounds by rapidly and effectively predicting the specific medicinal value components of <em>Cyperus esculentus</em>.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105317"},"PeriodicalIF":3.7000,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemometrics and Intelligent Laboratory Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169743925000024","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Screening bioactive molecules from natural plant compounds is currently a common approach in the field of drug discovery. Cyperus esculentus, a multipurpose crop primarily used for food, is highly valued in certain countries or regions for its unique medicinal properties. Although there is a foundational understanding of its components and pharmacological effects, exploration of its effective targets, especially kinase targets, remains insufficient. Our study integrates Artificial Intelligence-Assisted Drug Design (AIDD) by utilizing the KIBA and BindingDB datasets to train the GCNFG-DTA deep learning model for predicting the kinase target affinity of 152 active compounds from Cyperus esculentus. By screening for high-affinity molecule-kinase target pairs and employing molecular docking and molecular dynamics simulations, the study successfully identified pairs of the most promising active molecule-target combinations. Our predicting results demonstrate that the GCN-GAT-FG model, with its excellent predictive ability (Achieving a low MSE of 0.131 and a high CI of 0.896), significantly accelerates the discovery process of bioactive molecules. Further molecular docking validated that 15 high-affinity molecule-kinase target pairs had docking energy scores below −5 kJ/mol. Among these, 14 pairs exhibited stable conformations during 100 ns molecular dynamics simulations. Notably, Cyanidin chloride, N-Feruloyltyramine, and Imbricatonol were identified as the most promising molecules, demonstrating the high conformational stability when targeting the MAP3K8, CLK4 and FGR kinase targets, respectively. These findings provide a scientific basis for further exploring the medicinal potential of Cyperus esculentus. Overall, the deep learning method used in our study offers new insights into the field of drug discovery related to natural compounds by rapidly and effectively predicting the specific medicinal value components of Cyperus esculentus.
期刊介绍:
Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines.
Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data.
The journal deals with the following topics:
1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.)
2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered.
3) Development of new software that provides novel tools or truly advances the use of chemometrical methods.
4) Well characterized data sets to test performance for the new methods and software.
The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.