Abstract

Urease inhibitors are known to play a vital role in the field of medicine as well as agriculture. Special attention is attributed to the development of novel urease inhibitors with a view to treat the Helicobacter pylori infection. Amongst a number of urease inhibitors, a large number of molecules fail in vivo and in clinical trials due to their hydrolytic instability and toxicity profile. The search for potential inhibitors may require screening of large and diverse databases of small molecules and to design novel molecules. We developed a Monte-Carlo method-based QSAR model to predict urease inhibiting potency of molecules using SMILES and GRAPH descriptors on an existing diverse database of urease inhibitors. The QSAR model satisfies all the statistical parameters required for acceptance as a good model. The model is applied to identify urease inhibitors among the wide range of compounds in the phytochemical database, NPACT, as a test case. We combine the ligand-based and structure-based drug discovery methods to improve the accuracy of the prediction. The method predicts pIC₅₀ and estimates docking score of compounds in the database. The method may be applied to any other database or compounds designed in silico to discover novel drugs targeting urease.

Communicated by Ramaswamy H. Sarma

摘要

众所周知，脲酶抑制剂在医学和农业领域发挥着至关重要的作用。特别注意开发新型脲酶抑制剂以治疗幽门螺杆菌感染。在许多脲酶抑制剂中，大量分子在体内失效以及由于其水解不稳定性和毒性特征而在临床试验中。寻找潜在的抑制剂可能需要筛选大型和多样化的小分子数据库并设计新分子。我们开发了一个基于 Monte-Carlo 方法的 QSAR 模型，在现有的各种脲酶抑制剂数据库上使用 SMILES 和 GRAPH 描述符来预测分子的脲酶抑制效力。QSAR 模型满足被接受为良好模型所需的所有统计参数。该模型用于在植物化学数据库 NPACT 中的广泛化合物中识别脲酶抑制剂作为测试用例。我们结合了基于配体和基于结构的药物发现方法来提高预测的准确性。该方法预测 pIC ₅₀并估计数据库中化合物的对接分数。该方法可以应用于任何其他数据库或计算机设计的化合物，以发现靶向脲酶的新药。

由 Ramaswamy H. Sarma 传达