Nowadays, the importance of computational methods in the design of therapeutic agents in a more efficient way is indisputable. Particularly, these methods have been important in the design of novel acetylcholinesterase enzyme inhibitors related to Alzheimer’s disease. In this sense, in this report a computational model of linear prediction of acetylcholinesterase inhibitory activity of steroids and triterpenes is presented. The model is based in a correlation between binding energies obtained from molecular dynamic simulations (after docking studies) and \(\hbox {IC}_{{50}}\) values of a training set. This set includes a family of natural and semi-synthetic structurally related alkaloids reported in bibliography. These types of compounds, with some structural complexity, could be used as building blocks for the synthesis of many important biologically active compounds Therefore, the present study proposes an alternative based on the use of conventional and easily accessible tools to make progress on the rational design of molecules with biological activity.

如今，计算方法在以更有效的方式设计治疗剂中的重要性是无可争议的。特别是，这些方法在设计与阿尔茨海默病相关的新型乙酰胆碱酯酶抑制剂方面非常重要。从这个意义上说，在本报告中，提出了类固醇和三萜类化合物的乙酰胆碱酯酶抑制活性的线性预测计算模型。该模型基于从分子动力学模拟（对接研究后）获得的结合能与\(\hbox {IC}_{{50}}\)训练集的值。这组包括参考书目中报告的一系列天然和半合成结构相关的生物碱。这些类型的化合物具有一定的结构复杂性，可用作合成许多重要生物活性化合物的构建模块 因此，本研究提出了一种基于使用常规且易于获取的工具的替代方案，以在合理设计上取得进展具有生物活性的分子。