Abstract：
This study aims to identify potential focal adhesion kinase (FAK) inhibitors through an integrated computational approach, combining mol2vec descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation. A dataset of 437 compounds with known FAK inhibitory activities was used to develop QSAR models using machine learning algorithms combined with mol2vec descriptors. Subsequently, the most promising compounds were subjected to molecular docking against FAK to evaluate their binding affinities and key interactions. ADMET study and molecular dynamics simulation were also employed to investigate the pharmacokinetic, drug-like properties, and the stability of the protein-ligand complexes. The results showed that the mol2vec descriptor-based QSAR model established by support vector regression demonstrated good predictive performance (R2 = 0.813, RMSE = 0.453, MAE = 0.263 in case of training set, and R2 = 0.729, RMSE = 0.635, MAE = 0.477 in case of test set), indicating their reliability in identifying potent FAK inhibitors. Using this QSAR model and molecular docking, compound 21 (ZINC000004523722) was identified as the most potential compound, with predicted logIC50 value and binding energy of 2.59 and - 9.3 kcal/mol, respectively. The results of molecular dynamics simulation and ADMET study also further suggested its potential as a promising drug candidate. However, because our research was merely theoretical, additional in vitro and in vivo studies are required for the verification of these results.
摘要：
本研究旨在通过综合计算方法确定潜在的粘着斑激酶（FAK）抑制剂，结合基于Mol2vec描述符的QSAR，分子对接,ADMET研究，和分子动力学模拟。使用具有已知FAK抑制活性的437种化合物的数据集来开发使用机器学习算法结合mol2vec描述符的QSAR模型。随后，对最有前途的化合物进行分子对接,以评估它们的结合亲和力和关键相互作用.还采用ADMET研究和分子动力学模拟来研究药代动力学，类似药物的特性，以及蛋白质-配体复合物的稳定性。结果表明，支持向量回归建立的基于mole2vec描述符的QSAR模型表现出良好的预测性能（在训练集的情况下，R2=0.813，RMSE=0.453，MAE=0.263，R2=0.729,RMSE=0.635,MAE=0.477,表明它们在鉴定有效的FAK抑制剂方面的可靠性。利用这个QSAR模型和分子对接，化合物21(ZINC000004523722)被确定为最具潜力的化合物,预测logIC50值和结合能为2.59和-9.3kcal/mol，分别。分子动力学模拟和ADMET研究的结果也进一步表明了其作为有希望的候选药物的潜力。然而,因为我们的研究只是理论上的，需要额外的体外和体内研究来验证这些结果。
