Computational identification of novel c-MET receptor tyrosine kinase inhibitors in lung cancer through pharmacophore-based screening, docking studies, ADMET evaluation and Molecular dynamic simulations

This dataset contains pharmacophore modelling, molecular docking results, binding poses, and MD simulation trajectory files used in the study titled "Computational identification of novel c-MET receptor tyrosine kinase inhibitors in lung cancer through pharmacophore-based screening, docking studies, ADMET evaluation, and Molecular dynamic simulations". The study includes validation of pharmacophore models, Pharmacophore based virtual screening interaction plots, trajectory analysis outputs, and some figures used for validating binding stability. The data was generated using Maestro v. 9. 0 and GROMACS (2020), and supports the findings reported in the manuscript.

本数据集包含题为《基于药效团筛选、对接研究、ADMET（吸收-分布-代谢-排泄-毒性）评价及分子动力学模拟的肺癌新型c-MET受体酪氨酸激酶抑制剂的计算鉴定》的研究中所用的药效团（pharmacophore）建模、分子对接（molecular docking）结果、结合构象（binding poses）以及分子动力学（MD, Molecular Dynamics）模拟轨迹文件。该研究涵盖了药效团模型验证、基于药效团的虚拟筛选（virtual screening）相互作用图谱、轨迹分析（trajectory analysis）输出结果，以及若干用于验证结合稳定性（binding stability）的图表。本数据集通过Maestro v.9.0与GROMACS (2020)生成，可为该论文中报道的研究结论提供数据支持。