Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations.

IF 2.7 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling Pub Date : 2024-11-14 DOI:10.1016/j.jmgm.2024.108906

Quynh Mai Thai, Trung Hai Nguyen, George Binh Lenon, Huong Thi Thu Phung, Jim-Tong Horng, Phuong-Thao Tran, Son Tung Ngo

引用次数: 0

Abstract

Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynamics calculations were employed to characterize the potential inhibitors for AChE from MedChemExpress (MCE) database. The trained ML model was initially employed for estimating the inhibitory of MCE compounds. Atomistic simulations including molecular docking and molecular dynamics simulations were then used to confirm ML outcomes. In particular, the physical insights into the ligand binding to AChE were clarified over the calculations. Two compounds, PubChem ID of 130467298 and 132020434, were indicated that they can inhibit AChE.

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通过机器学习和原子计算从 MCE 数据库中估算 AChE 抑制剂。

乙酰胆碱酯酶（AChE）是治疗阿尔茨海默病（AD）最成功的靶点之一。抑制乙酰胆碱酯酶可以预防阿尔茨海默病。在这种情况下，我们采用了机器学习（ML）模型、分子对接和分子动力学计算来表征 MedChemExpress（MCE）数据库中 AChE 的潜在抑制剂。训练有素的 ML 模型最初用于估计 MCE 化合物的抑制性。然后使用原子模拟（包括分子对接和分子动力学模拟）来确认 ML 结果。特别是，通过计算，阐明了配体与 AChE 结合的物理原理。结果表明，PubChem ID 为 130467298 和 132020434 的两种化合物可以抑制 AChE。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of molecular graphics & modelling 生物-计算机：跨学科应用

CiteScore

5.50

自引率

6.90%

发文量

216

审稿时长

35 days

期刊介绍： The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.