Abstract：
This study aimed to use a computational approach that combined the classification-based QSAR model, molecular docking, ADME studies, and molecular dynamics (MD) to identify potential inhibitors of Fyn kinase. First, a robust classification model was developed from a dataset of 1,078 compounds with known Fyn kinase inhibitory activity, using the XGBoost algorithm. After that, molecular docking was performed between potential compounds identified from the QSAR model and Fyn kinase to assess their binding strengths and key interactions, followed by MD simulations. ADME studies were additionally conducted to preliminarily evaluate the pharmacokinetics and drug-like characteristics of these compounds. The results showed that our obtained model exhibited good predictive performance with an accuracy of 0.95 on the test set, affirming its reliability in identifying potent Fyn kinase inhibitors. Through the application of this model in conjunction with molecular docking and ADME studies, nine compounds were identified as potential Fyn kinase inhibitors, including 208 (ZINC70708110), 728 (ZINC8792432), 734 (ZINC8792187), 736 (ZINC8792350), 738 (ZINC8792286), 739 (ZINC8792309), 817 (ZINC33901069), 852 (ZINC20759145), and 1227 (ZINC100006936). MD simulations further demonstrated that the four most promising compounds, 728, 734, 736, and 852 exhibited stable binding with Fyn kinase during the simulation process. Additionally, a web-based platform ( https://fynkinase.streamlit.app/ ) has been developed to streamline the screening process. This platform enables users to predict the activity of their substances of interest on Fyn kinase from their SMILES, using our classification-based QSAR model and molecular docking.
摘要：
本研究旨在使用一种结合基于分类的QSAR模型的计算方法，分子对接,ADME研究,和分子动力学（MD）来鉴定Fyn激酶的潜在抑制剂。首先,从1,078个具有已知Fyn激酶抑制活性的化合物的数据集中开发了一个稳健的分类模型，使用XGBoost算法。之后,在从QSAR模型鉴定的潜在化合物和Fyn激酶之间进行分子对接，以评估它们的结合强度和关键相互作用，其次是MD模拟。另外进行ADME研究以初步评估这些化合物的药代动力学和药物样特征。结果表明，我们获得的模型在测试集上表现出良好的预测性能，精度为0.95，确认其在鉴定有效的Fyn激酶抑制剂方面的可靠性。通过该模型与分子对接和ADME研究相结合的应用，九种化合物被鉴定为潜在的Fyn激酶抑制剂，包括208（ZINC70708110），728(ZINC8792432),734(ZINC8792187),736(ZINC8792350),738(ZINC8792286),739(ZINC8792309),817(ZINC33901069),852(ZINC20759145),和1227（锌100006936）。MD模拟进一步证明了四种最有前途的化合物，728、734、736和852在模拟过程中表现出与Fyn激酶的稳定结合。此外,基于Web的平台（https://fynkinase.流光。app/)的开发是为了简化筛选过程。该平台使用户能够从他们的SMILES预测他们感兴趣的物质对Fyn激酶的活性，使用我们基于分类的QSAR模型和分子对接。
