Computational approach to the discovery of potential neprilysin inhibitors compounds for cardiovascular diseases treatment

Cañizares-Carmenate, Yudith; Cárdenas, Adriana Alcántara; Llerena, Viviana Roche; Torrens, Francisco; Castillo-Garit, Juan A.

doi:10.1007/s00044-020-02529-0

Cited by 2 publications

(1 citation statement)

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“…Quantitative Structure Activity Relationship (QSAR) and molecular simulations are widely used in drug design for providing critical and superlative evidence for lead/drug optimization 35 . QSAR investigations replace expensive synthesis and bioassay with computational models to accelerate drug selection in the early stages of development 36 . High-throughput virtual screening can be accelerated in the drug development process by using molecular docking and dynamics approaches 37 .…”

Section: Introductionmentioning

confidence: 99%

Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors

Shah,

Chaple,

Masand

et al. 2024

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%