Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations

Elidrissi, B.; Ousaa, Abdellah; Ghamali, Mounir; Chtita, Samir; Ajana, Mohammed Aziz; Bouachrine, Mohammed; Lakhlifi, Tahar

doi:10.4018/ijqspr.2018010107

Cited by 5 publications

(5 citation statements)

References 22 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several models have been designed for ARV therapies. − Almost all models and studies are designed specifically for HIV ARV drugs and just a few for HBV. We have not found ARV ML models for the rest of the retroviruses included in this study.…”

Section: Resultsmentioning

confidence: 99%

“…The use of EuResist database presented a limitation, as not all desired information was present at the moment of study. Afantitis’s and Elidrissi’s groups developed models for 80 and 35 cases, respectively. , Both used multiple linear regression analysis. The first work selected descriptors for a 37 case initial pool to create a 5-parameter model to calculate HEPT derivatives with high anti-HIV activity.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

et al. 2019

View full text Add to dashboard Cite

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c 0 ), 55 protein accessions (c 1 ), 83 cell lines (c 2 ), 64 organisms of assay (c 3 ), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

et al. 2019

View full text Add to dashboard Cite

show abstract

“…These methods have become very popular in recent years because they can reach similar precision to other methods in less time and less cost from the computational point of view. In agreement with the DFT results, energy of the fundamental state of a polyelectronic system can be expressed through the total electronic density, and in fact, the use of electronic density instead of wave function for calculating the energy constitutes the fundamental base of DFT [30,31] using the B3LYP functional [32] and a 6-31G(d) basis set. The B3LYP, a version of DFT method, uses Becke's three-parameter functional (B3) and includes a mixture of HF with DFT exchange terms associated with the gradient corrected correlation functional of Lee, Yang and Parr (LYP).…”

Section: Calculation Of Molecular Descriptorsmentioning

confidence: 90%

QSPR and DFT Studies on the Melting Point of Carbocyclic Nitroaromatic Compounds

Elidrissi¹,

Ousaa²,

Ghamali³

et al. 2017

J Phys Chem Biophys

View full text Add to dashboard Cite

“…The sensitive detection of hazardous materials is important owing to increasing concern regarding human health, public security, and environmental monitoring. [51][52][53] The increasing demands of society, modern agriculture, pharmaceutical science, and other industrial activities could not have been met without the major contribution of organic solvents, pesticides, and aromatic compounds. [54,55] Furthermore, the extensive use of hazardous substances in chemical and biochemical processes is a major concern in various scientific fields globally, including the health, medical, and environmental industries.…”

Section: Toxic Chemicals and Their Detectionmentioning

confidence: 99%

Mechanistic Insight into Charge and Energy Transfers of Luminescent Metal–Organic Frameworks Based Sensors for Toxic Chemicals

Mohan

Kumar

et al. 2021

Advanced Sustainable Systems

View full text Add to dashboard Cite

The success of MOFs has driven their development as a new tool for sensing analytes including toxic chemicals. [19][20][21] The MOF sensors can be applied in both environmental and biological systems. [22] The MOF sensor field has been established with the development of sensors for different metal ions, anions, and molecules. [23,24] The potential application of MOF sensors to the detection of heavy metal ions, toxic anions, and other hazardous species is of great interest. [25,26] Specifically, luminescence technology has allowed the roles of various MOF sites in analyte capture to be easily studied. [27][28][29] The precise use of MOFs is potentially a good tool for analyte detection. Furthermore, MOFs have been established as potential materials for selective, fast, and portable tools for sensing toxic chemicals, with the advantage of light-emitting properties. [30,31] Interestingly, these sensing models can operate in water and are pH stable with high efficiency. [32] Furthermore, MOFs are wellknown materials for the degradation of toxic chemicals. [33,34] Several reports have been published on MOF sensors for toxicant chemicals, such as metal ions, anions, organic solvents, pesticides, nitroaromatic compounds (NACs), chemical warfare agents, [35] and others. [36][37][38] Some reviews have summarized applications of MOFs in sensing and detection probes for ions and volatile organic compounds, [39] and MOF sensors in pesticide detection and absorption with their classification. [40] Others have summarized and analyzed interactions between hazardous compound adsorbates and MOFs. [41,42] The stability and applications of MOFs have been summarized by the research groups of Ding and coworkers. [43] Pehlivan and et al. summarized the role of fluorescence resonance energy transfer in elucidating molecular interactions utilized in biosensing tools. [44,45] Besides luminescence sensing, the reviewers summarized the progress of MOFs relevance in the various area including environment and medical science. [46] Recently, Liu and coworkers summarized the progress of MOFs and discussed the remaining challenges in drug delivery, [47] Garcia and coworkers studied and compared the MOFs as photocatalysts with common inorganic photocatalysts, [48] and Manos and coworkers studied and summarized MOFs challenges and prospects for ion-exchange and sorption applications. [49] Despite these meaningful reviews, the factors responsible for signal changes, mechanisms, and limits of detection have Mechanistic studies of materials able to detect hazardous and toxic chemicals, such as common organic solvents, pesticides, and nitroaromatic compounds (NACs), are critically important owing to the threat they pose to humans and the environment. Recently, porous luminescent metal-organic frameworks (MOFs) with multifunctional sites, high surface areas, and structural tunability have emerged as sensory devices for the detection of hazardous and toxic chemicals. Herein, recent progress regarding the mechanistic behavior of MOF sensors in the det...

show abstract

Study of Pyrimidine-4-carboxamide Derivatives as HIV-1 Integrase Inhibitors Using QSAR and DFT Calculations

Cited by 5 publications

References 22 publications

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

QSPR and DFT Studies on the Melting Point of Carbocyclic Nitroaromatic Compounds

Mechanistic Insight into Charge and Energy Transfers of Luminescent Metal–Organic Frameworks Based Sensors for Toxic Chemicals

Contact Info

Product

Resources

About