Design, synthesis, in-vitro, in-silico, DFT and POM studies of a novel family of sulfonamides as potent anti-triple-negative breast cancer agents.

In this study, a new family of ethacrynic acid-sulfonamides and indazole-sulfonamides was synthesized and tested in vitro against MDA-MB-468 triple-negative breast cancer cells and PBMCs human peripheral blood mononuclear cells, using the MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay. The aim of this research is to discover novel compounds with potential therapeutic effects on breast cancer. The antiproliferative activity of these compounds showed a significant dose-dependent activity, with IC values ranging between 2.

Design, Synthesis, Computational Studies, and Anti-Proliferative Evaluation of Novel Ethacrynic Acid Derivatives Containing Nitrogen Heterocycle, Urea, and Thiourea Moieties as Anticancer Agents.

In the present work, the synthesis of new ethacrynic acid () derivatives containing nitrogen heterocyclic, urea, or thiourea moieties via efficient and practical synthetic procedures was reported. The synthesised compounds were screened for their anti-proliferative activity against two different cancer cell lines, namely, HL60 (promyelocytic leukaemia) and HCT116 (human colon carcinoma). The results of the in vitro tests reveal that compounds -, , (-), and exhibit potent anti-proliferative activity against the HL60 cell line, with values of the percentage of cell viability ranging from 20 to 35% at 1 μM of the drug and IC values between 2.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations.

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives.

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach.

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity.

Structure-based virtual screening, ADMET analysis, and molecular dynamics simulation of Moroccan natural compounds as candidates for the SARS-CoV-2 inhibitors.

The lack of treatments and vaccines effective against SARS-CoV-2 has forced us to explore natural compounds that could potentially inhibit this virus. Additionally, Morocco is renowned for its rich plant diversity and traditional medicinal uses, which inspires us to leverage our cultural heritage and the abundance of natural resources in our country for therapeutic purposes. In this study, an extensive investigation was conducted to gather a collection of phytoconstituents extracted from Moroccan plants, aiming to evaluate their ability to inhibit the proliferation of the SARS-CoV-2 virus.

A Novel 3D Reversible Data Hiding Scheme Based on Integer-Reversible Krawtchouk Transform for IoMT.

To avoid rounding errors associated with the limited representation of significant digits when applying the floating-point Krawtchouk transform in image processing, we present an integer and reversible version of the Krawtchouk transform (IRKT). This proposed IRKT generates integer-valued coefficients within the Krawtchouk domain, seamlessly aligning with the integer representation commonly utilized in lossless image applications. Building upon the IRKT, we introduce a novel 3D reversible data hiding (RDH) algorithm designed for the secure storage and transmission of extensive medical data within the IoMT (Internet of Medical Things) sector.

Repositioning Cannabinoids and Terpenes as Novel EGFR-TKIs Candidates for Targeted Therapy Against Cancer: A virtual screening model using CADD and biophysical simulations.

This study examines the potential of L. plants to be repurposed as therapeutic agents for cancer treatment through designing of hybrid Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). A set of 50 phytochemicals was taken from Cannabinoids and Terpenes and subjected for screening using Semi-flexible and Flexible Molecular Docking methods, MM-GBSA free binding energy computations, and pharmacokinetic/pharmacodynamic (ADME-Tox) predictions.

Study of the competition between Pi and Cr (VI) for the use of Pi-transporter at Vicia faba L. using molecular modeling.

Recent studies have shown that Cr uses other element transporters such as phosphate transporters to enter cells. The aim of this work is to explore the interaction between dichromate and inorganic phosphate (Pi) in the plant of Vicia faba L. To study this interaction, we used three concentrations of Dipotassium hydrogen phosphate (KHPO) 10 mM (Pi10), 50 mM (Pi50) and 100 mM (Pi100) added alone or in combination with potassium dichromate (KCrO) Cr + Pi10, Cr + Pi50 and Cr + Pi100.

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach.

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells.

Combined computational approaches for developing new anti-Alzheimer drug candidates: 3D-QSAR, molecular docking and molecular dynamics studies of liquiritigenin derivatives.

Butyrylcholinesterase is an acetylcholine-degrading enzyme involved in the memorization process, which is becoming an interesting target for the symptomatic treatment of Alzheimer's disease. In the present investigation, the structure-activity relationship of a set of Liquiritigenin derivatives recently revealed to be Butyrylcholinesterase inhibitors was studied basing on comparative field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMISA). As a result, performant models with high predictive capability have been developed (CoMFA model: R = 0.

A computer-aided drug design approach to explore novel type II inhibitors of c-Met receptor tyrosine kinase for cancer therapy: QSAR, molecular docking, ADMET and molecular dynamics simulations.

Small molecules such as 4-phenoxypyridine derivatives have remarkable inhibitory activity against c-Met enzymatic activity and proliferation of cancer cell lines. Since there is a relationship between structure and biological activity of these molecules, these little compounds may have great potential for clinical pharmaceutical use against various types of cancer caused by c-Met activity. The purpose of this study was to remodel the structures of 4-phenoxypyridine derivatives to achieve strong inhibitory activity against c-Met and provide favorable pharmacokinetic properties for drug design and discovery.

Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors.

FLT3 is considered a potential target of acute myeloid leukemia therapy. In this study, we applied a computer-aided methodology unifying molecular docking and pharmacophore screening to identify potent inhibitors against FLT3. To investigate the pharmacophore area and binding mechanism of FLT3, the reported co-crystallized Gilteritinib ligand was docked into the active site using Glide XP.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CL enzyme for COVID-19 therapy: a computer-aided drug design approach.

Unlabelled: Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CL and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CL and favorable pharmacokinetic properties for drug design and discovery.

Optimal reconstruction and compression of signals and images by Hahn moments and artificial bee Colony (ABC) algorithm.

In this paper, we present an efficient and optimal method for optimization of Hahn parameters and using the Artificial Bee Colony algorithm (ABC) in order to improve the quality of reconstruction and the compression of bio-signals and color images of large sizes. The proposed methods are essentially based on two concepts: the development of a recursive calculation of the initial terms of Hahn polynomials in order to avoid the problems of instability of polynomial values and the use of ABC algorithm to optimize the values of the parameters and of the discrete orthogonal Hahn polynomials () during the reconstruction and the compression of bio-signals and color images. The simulation results performed on bio-signals and on large size / color images clearly show the efficiency and superiority of the proposed methods over conventional methods in terms of reconstruction of signals and images.

Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies.

In the present study, a quantitative relationship between the biological inhibitory activity of alpha-amylase and molecular structures of novel benzimidazole derivatives is analyzed in silico. The best QSAR model screened via MLR technique indicated that the exact mass, topological diameter and numerical rotational bonding structural properties of benzimidazole derivatives highly affect the bioactivity of these compounds against α-amylase. Based on the structural properties identified via linear QSAR model favorable for improving pIC50 of benzimidazole derivatives, fourteen new molecules bearing benzimidazole radicals were designed and their biological inhibitory activity against α-amylase was improved.

Robust 2D and 3D images zero - watermarking using dual Hahn moment invariants and Sine Cosine Algorithm.

In this paper, we initially provide significant improvements on the computational aspects of dual Hahn moment invariants (DHMIs) in both 2D and 3D domains. These improvements ensure the numerical stability of DHMIs for large-size images. Then, we propose an efficient method for optimizing the local parameters of dual Hahn polynomials (DHPs) when computing DHMIs using the Sine-Cosine Algorithm (SCA).

3D-QSAR, ADME-Tox, and molecular docking of semisynthetic triterpene derivatives as antibacterial and insecticide agents.

In the present work, 27 triterpene derivatives have been subjected to 3D-QSAR, ADME-Tox, and molecular docking for their insecticidal activity. The selected derivatives are previously semi-synthesized based on compounds obtained from and latex. The in silico studies were used to predict and to evaluate the antibacterial and insecticidal properties of the 3D structure of triterpene derivatives.

Fast and stable computation of higher-order Hahn polynomials and Hahn moment invariants for signal and image analysis.

This article presents, on the one hand, new algorithms for the fast and stable computation of discrete orthogonal Hahn polynomials of high order (HPs) based on the elimination of all gamma and factorial functions that cause the numerical fluctuations of HPs, and based on the use of appropriate stability conditions. On the other hand, a new method for the fast and numerically stable computation of Hahn moment invariants (HMIs) is also proposed. This method is mainly based on the use of new recursive relations of HPs and of matrix multiplications when calculating HMIs.

QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase.

A quantitative structure-activity relationship (QSAR) study is performed on 48 novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2 derivatives as anticancer agents capable of inhibiting c-Met receptor tyrosine kinase. The present study is conducted using multiple linear regression, multiple nonlinear regression and artificial neural networks. Three QSAR models are developed after partitioning the database into two sets (training and test) via the k-means method.