Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus

Molecules

Rachid Daoud¹  Achraf El Allali¹  Wissal Liman¹  Ismail Hdoufane²  Driss Cherqaoui²  Mehdi Oubahmane²  Didier Villemin³  Imane Bjij⁴ 

[1]African Genome Center, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco

[2]Department of Chemistry, Faculty of Sciences Semlalia, BP 2390, Marrakech 40000, Morocco

[3]Ecole Nationale Supérieure d’Ingénieurs (ENSICAEN) Laboratoire de Chimie Moléculaire et Thioorganique, UMR 6507 CNRS, INC3M, FR3038, Labex EMC3, Labex SynOrg ENSICAEN & Université de Caen, 14118 Caen, France

[4]Institut Supérieur des Professions Infirmières et Techniques de Santé (ISPITS), Dakhla 73000, Morocco

关键词: chemoinformatics;    drug discovery;    molecular descriptors;    QSAR;    HCV;    NS5A;   

DOI  :  10.3390/molecules27092729

来源: DOAJ

【 摘 要 】

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure–activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R² = 0.991 and Q² = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R² = 0.915 and Q² = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

【 授权许可】

Unknown