GCNFG-DTA:Screening natural medicinal components of Cyperus esculentus targeting kinases with AIDD methods

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. •We proposed GCNFG-DTA, importing pharmacophore embeddings for enhanced molecular representation and improved performance.•We used GCNFG-DTA to investigate Cyperus esculentus components with kinases, predicting 2168 pairs above the threshold.•MD validation showed 14/22 were stable, with Cyanidin chloride, N-Feruloyltyramine, and Imbricatonol most promising.•GCNFG-DTA's success in screening Cyperus esculentus components offers a new paradigm to uncovering medicinal values in foods.