Publications by authors named "Amit Kumar Halder"

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism.

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Non-alcoholic fatty liver disease (NAFLD), with a global prevalence of 25%, continues to escalate, creating noteworthy concerns towards the global health burden. NAFLD causes triglycerides and free fatty acids to build up in the liver. The excessive fat build-up causes inflammation and damages the healthy hepatocytes, leading to non-alcoholic steatohepatitis (NASH).

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Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors.

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Six drugs (dapsone, diltiazem, timolol, rosiglitazone, mesalazine, and milnacipran) that were predicted by network-based polypharmacology approaches as potential anti-Alzheimer's drugs, have been subjected in this study for and evaluation to check their potential against protein fibrillation, which is a causative factor for multiple diseases such as Alzheimer's disease, Parkinson's disease, Huntington disease, cardiac myopathy, type-II diabetes mellitus and many others. Molecular docking and thereafter molecular dynamics (MD) simulations revealed that diltiazem, rosiglitazone, and milnacipran interact with the binding residues such as Asp52, Glu35, Trp62, and Asp101, which lie within the fibrillating region of HEWL. The MM-GBSA analysis revealed -7.

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Diabetes mellitus (DM) is a chronic metabolic disorder characterized by hyperglycemic state. The α-glucosidase and α-amylase are considered two major targets for the management of Type 2 DM due to their ability of metabolizing carbohydrates into simpler sugars. In the current study, cheminformatics analyses were performed to develop validated and predictive models with a dataset of 187 α-glucosidase and α-amylase dual inhibitors.

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Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors.

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Manufactured substances known as endocrine disrupting chemicals (EDCs) released in the environment, through the use of cosmetic products or pesticides, can cause severe eco and cytotoxicity that may induce trans-generational as well as long-term deleterious effects on several biological species at relatively low doses, unlike other classical toxins. As the need for effective, affordable and fast EDCs environmental risk assessment has become increasingly pressing, the present work introduces the first moving average-based multitasking quantitative structure-toxicity relationship (MA-mtk QSTR) modeling specifically developed for predicting the ecotoxicity of EDCs against 170 biological species belonging to six groups. Based on 2,301 data-points with high structural and experimental diversity, as well as on the usage of various advanced machine learning methods, the novel most predictive QSTR models display overall accuracies > 87% in both training and prediction sets.

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Introduction: Major depressive disorders (MDD) pose major health burdens globally. Currently available medications have their limitations due to serious adverse effects, long latency periods as well as resistance. Considering the highly complicated pathological nature of this disorder, it has been suggested that multitarget drugs or multi-target-directed ligands (MTDLs) may provide long-term therapeutic solutions for the treatment of MDD.

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Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR.

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RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations.

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Histone deacetylases (HDACs) are enzymes that play a role in chromatin remodeling and epigenetics. They belong to a specific category of enzymes that eliminate the acetyl part of the histones' -N-acetyl lysine, causing the histones to be wrapped compactly around DNA. Numerous biological processes rely on HDACs, including cell proliferation and differentiation, angiogenesis, metastasis, gene regulation, and transcription.

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Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs.

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Conventional in silico modeling is often viewed as 'one-target' or 'single-task' computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box-Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology.

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Objective: Acrylic acid derivatives are frequently used as dental monomers and their cytotoxicity towards various cell lines is well documented. This study aims to probe the structural and physicochemical attributes responsible for higher toxicity of dental monomers, using quantitative structure-activity relationships (QSAR) modeling approaches.

Methods: A regression-based linear single-target QSAR (st-QSAR) model was developed with a comparatively small dataset containing 39 compounds, the cytotoxicity of which has been assessed over the Hela S3 cell line.

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The inhibitors of two isoforms of mitogen-activated protein kinase-interacting kinases (i.e., MNK-1 and MNK-2) are implicated in the treatment of a number of diseases including cancer.

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Our previous results suggest that phenyl/naphthylacetyl pentanoic acid derivatives may exhibit dual MMP-2 and HDAC8 inhibitory activities and show effective cytotoxic properties. Here, 13 new compounds () were synthesized and characterized. Along with these new compounds, 16 previously reported phenyl/napthylacetyl pentanoic acid derivatives () were biologically evaluated.

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Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES-and because the vast majority of DES has yet to be synthesized-the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES.

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AKT, is a serine/threonine protein kinase comprising three isoforms-namely: AKT1, AKT2 and AKT3, whose inhibitors have been recognized as promising therapeutic targets for various human disorders, especially cancer. In this work, we report a systematic evaluation of multi-target Quantitative Structure-Activity Relationship (mt-QSAR) models to probe AKT' inhibitory activity, based on different feature selection algorithms and machine learning tools. The best predictive linear and non-linear mt-QSAR models were found by the genetic algorithm-based linear discriminant analysis (GA-LDA) and gradient boosting (Xgboost) techniques, respectively, using a dataset containing 5523 inhibitors of the AKT isoforms assayed under various experimental conditions.

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Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multitarget QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling.

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Nanomaterials (NMs) are an ever-increasing field of interest, due to their wide range of applications in science and technology. However, despite providing solutions to many societal problems and challenges, NMs are associated with adverse effects with potential severe damages towards biological species and their ecosystems. Particularly, it has been confirmed that NMs may induce serious genotoxic effects on various biological targets.

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Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure-activity relationship (mt-QSAR) models based on the Box-Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions.

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The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure-activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database.

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Simultaneous inhibition of MMP-2 and HDAC8 may be an effective strategy to target cancer. In continuation of our earlier efforts, a series of substituted pentanoic acids (-) were synthesized and checked for their biological activity along with some earlier reported compounds (). Compounds and were found to induce apoptosis effectively in a dose-dependent fashion in Jurkat-E6.

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Quantitative structure-activity relationships (QSAR) modeling is a well-known computational technique with wide applications in fields such as drug design, toxicity predictions, nanomaterials, etc. However, QSAR researchers still face certain problems to develop robust classification-based QSAR models, especially while handling response data pertaining to diverse experimental and/or theoretical conditions. In the present work, we have developed an open source standalone software "QSAR-Co" (available to download at https://sites.

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Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process.

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