心血管疾病,包括心力衰竭,中风,和高血压,影响全球6.08亿人,造成32%的死亡。60%的患者需要联合治疗,同时涉及肾素-血管紧张素-醛固酮系统(RAAS)和脑啡肽抑制。这项研究引入了一种新颖的多靶标计算机建模技术(mt-QSAR)来评估对Neprilysin和血管紧张素转换酶的抑制潜力。使用线性(GA-LDA)和非线性(RF)算法,使用983种化学物质开发mt-QSAR分类模型,以预测对Neprilysin和血管紧张素转换酶的抑制作用。Box-Jenkins方法,特征选择方法,和机器学习算法被用来获得最具预测性的模型,总体准确率约为90%。此外,这项研究采用了设计支架的虚拟筛选(查尔酮及其类似物,1,3-噻唑,1,3,4-噻二唑)应用开发的mt-QSAR模型和分子对接。识别出的虚拟命中经历了连续的过滤步骤,纳入对药物相似性的评估,ADMET简介,和合成的无障碍工具。最后,然后使用分子动力学模拟来鉴定和排列最有利的化合物。从这项研究中获得的数据可能为新的多靶向心血管抑制剂的鉴定提供关键方向。
Cardiovascular diseases, including heart failure, stroke, and hypertension, affect 608 million people worldwide and cause 32% of deaths. Combination therapy is required in 60% of patients, involving concurrent Renin-Angiotensin-Aldosterone-System (RAAS) and
Neprilysin inhibition. This study introduces a novel multi-target in-silico modeling technique (mt-QSAR) to evaluate the inhibitory potential against
Neprilysin and Angiotensin-converting enzymes. Using both linear (GA-LDA) and non-linear (RF) algorithms, mt-QSAR classification models were developed using 983 chemicals to predict inhibitory effects on
Neprilysin and Angiotensin-converting enzymes. The Box-Jenkins method, feature selection method, and machine learning algorithms were employed to obtain the most predictive model with ~ 90% overall accuracy. Additionally, the study employed virtual screening of designed scaffolds (Chalcone and its analogues, 1,3-Thiazole, 1,3,4-Thiadiazole) applying developed mt-QSAR models and molecular docking. The identified virtual hits underwent successive filtration steps, incorporating assessments of drug-likeness, ADMET profiles, and synthetic accessibility tools. Finally, Molecular dynamic simulations were then used to identify and rank the most favourable compounds. The data acquired from this study may provide crucial direction for the identification of new multi-targeted cardiovascular inhibitors.