Bioprotective Role of Phytocompounds Against the Pathogenesis of Non-alcoholic Fatty Liver Disease to Non-alcoholic Steatohepatitis: Unravelling Underlying Molecular Mechanisms.

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). Dietary habits, obesity, insulin resistance, type 2 diabetes, and dyslipidemia influence NAFLD progression. The disease burden is complicated due to the paucity of therapeutic interventions. Obeticholic acid is the only approved therapeutic agent for NAFLD. With more scientific enterprise being directed towards the understanding of the underlying mechanisms of NAFLD, novel targets like lipid synthase, farnesoid X receptor signalling, peroxisome proliferator-activated receptors associated with inflammatory signalling, and hepatocellular injury have played a crucial role in the progression of NAFLD to NASH. Phytocompounds have shown promising results in modulating hepatic lipid metabolism and de novo lipogenesis, suggesting their possible role in managing NAFLD. This review discusses the ameliorative role of different classes of phytochemicals with molecular mechanisms in different cell lines and established animal models. These compounds may lead to the development of novel therapeutic strategies for NAFLD progression to NASH. This review also deliberates on phytomolecules undergoing clinical trials for effective management of NAFLD.

In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design.

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. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor-ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.

Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?

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. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.

Predicting the Surface Tension of Deep Eutectic Solvents: A Step Forward in the Use of Greener Solvents.

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. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including 'mixtures-out'- and 'compounds-out'-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.

AKT Inhibitors: The Road Ahead to Computational Modeling-Guided Discovery.

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. The linear model highlighted the key structural attributes responsible for higher inhibitory activity whereas the non-linear model displayed an overall accuracy higher than 90%. Both these predictive models, generated through internal and external validation methods, were then used for screening the Asinex kinase inhibitor library to identify the most potential virtual hits as pan-AKT inhibitors. The virtual hits identified were then filtered by stepwise analyses based on reverse pharmacophore-mapping based prediction. Finally, results of molecular dynamics simulations were used to estimate the theoretical binding affinity of the selected virtual hits towards the three isoforms of enzyme AKT. Our computational findings thus provide important guidelines to facilitate the discovery of novel AKT inhibitors.

Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures.

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. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models.

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.google.com/view/qsar-co ) to setup classification-based QSAR models that allow mining the response data coming from multiple conditions. The software comprises two modules: (1) the Model development module and (2) the Screen/Predict module. This user-friendly software provides several functionalities required for developing a robust multitasking or multitarget classification-based QSAR model using linear discriminant analysis or random forest techniques, with appropriate validation, following the principles set by the Organisation for Economic Co-operation and Development (OECD) for applying QSAR models in regulatory assessments.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool.

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. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.

Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents.

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. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

Structural findings of cinnolines as anti-schizophrenic PDE10A inhibitors through comparative chemometric modeling.

Schizophrenia is a complex psychiatric disorder associated with the distortion of striatopallidal neurotransmission of central nervous system. Phosphodiesterase10A (PDE10A) enzyme plays crucial role in cellular signaling pathways in schizophrenia. Inhibition of this enzyme may facilitate better treatment of this disease. 2D-QSAR, HQSAR, pharmacophore mapping, molecular docking, and 3D-QSAR analyses were performed on 81 cinnoline derivatives having PDE10A inhibitory activity. 2D-QSAR models were developed by multiple linear regression and partial least square analyses using both atom based and whole molecular descriptors. The best model, having considerable internal (q(2) = 0.812) and external (R(2)(pred) = 0.691) predictabilities, demonstrated importance of atom-based topological and whole molecular E-state as well as 3D topological indices. The best HQSAR model was also found to be statistically significant (q(2) = 0.664, R(2)(pred) = 0.513) and it highlighted some important structural features. PHASE-based pharmacophore hypothesis showed the importance of three hydrogen bond acceptor and one each of ring aromatic and hydrophobic features for higher activity. 3D-QSAR CoMFA and CoMSIA models were generated on two different types of alignment procedures-(1) pharmacophore (PHASE) based and (2) docking (GLIDE) based. GLIDE-based alignment produced better results for both CoMFA (Q(2) = 0.578; R(2)(pred) = 0.841) and CoMSIA (Q(2) = 0.610; R(2)(pred) = 0.824) methods. Molecular dynamics (MDs) simulations were performed for two ligand-receptor complexes and these simulations explored some crucial factors for higher activity. These findings of MD simulations were consistent with the interpretations obtained from other methods of analyses. The current study may help in designing new PDE10A inhibitors of this class.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction.

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. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.

Finding structural requirements of structurally diverse α-glucosidase and α-amylase inhibitors through validated and predictive 2D-QSAR and 3D-QSAR analyses.

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. Separate linear, interpretable and statistically robust 2D-QSAR models were constructed with datasets containing the activities of α-glucosidase and α-amylase inhibitors with an aim to explain the crucial structural and physicochemical attributes responsible for higher activity towards these targets. Consequently, some descriptors of the models pointed out the importance of specific structural moieties responsible for the higher activities for these targets and on the other hand, properties such as ionization potential and mass of the compounds as well as number of hydrogen bond donors in molecules were found to be crucial in determining the binding potentials of the dataset compounds. Statistically significant 3D-QSAR models were developed with both α-glucosidase and α-amylase inhibition datapoints to estimate the importance of 3D electrostatic and steric fields for improved potentials towards these two targets. Molecular docking performed with selected compounds with homology model of α-glucosidase and X-ray crystal structure of α-amylase largely supported the interpretations obtained from the cheminformatic analyses. The current investigation should serve as important guidelines for the design of future α-glucosidase and α-amylase inhibitors. Besides, the current investigation is entirely performed by using non-commercial open-access tools to ensure easy accessibility and reproducibility of the investigation which may help researchers throughout the world to work more on drug design and discovery.

Unveiling structural determinants for FXR antagonism in 1,3,4-trisubstituted-Pyrazol amide derivatives: A multi-scale in silico modelling approach.

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. By means of 2D-QSAR (quantitative structure-activity relationships) modelling techniques, we elucidated the key structural elements responsible for the antagonistic properties of these derivatives. We then employed QPhAR, an open-access software, to identify key molecular features within the compounds that enhance their antagonistic activity. Additionally, 3D-QSAR modelling allowed us to analyse the steric and electrostatic fields of aligned 3D structures, further refining our understanding of structure-activity relationships. Subsequent molecular dynamics simulations provided insights into the binding mode interactions between the compounds and FXR, with varying potencies, confirming and complementing the findings from 2D-QSAR, pharmacophore, and 3D-QSAR modelling. Particularly, our study highlighted the significance of hydrophobic interactions in conferring potent antagonism by the 1,3,4-trisubstituted-pyrazol amide derivatives against FXR. Overall, this work underscores the potential of 1,3,4-trisubstituted-pyrazol amides as FXR antagonists for NAFLD treatment. Notably, our reliance on open-access software fosters reproducibility and broadens the accessibility of our findings.

Structural insights into the interactions of repositioning and known drugs for Alzheimer's disease with hen egg white lysozyme by MM-GBSA.

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 in silico and in vitro 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 -7.86, -5.05, and -10.29 kcal/mol as the binding energy of diltiazem, rosiglitazone, and milnacipran. The RMSD and RMSF calculations revealed significant stabilities of these ligands within the binding pocket of HEWL. While compared with two reported ligands inhibiting HEWL fibrillation, milnacipran depicted almost similar binding potential with one of the known ligands (Ligand binding affinity -10.66 kcal/mol) of HEWL. Furthermore, secondary structure analyses revealed notable inhibition of the secondary structural changes with our candidate ligand; especially regarding retention of the 3/10 α-helix both by DSSP simulation, Circular dichroism, and FESEM-based microscopic image analyses. Taking further into experimental validation, all three ligands inhibited fibrillation in HEWL in simulated conditions as revealed by blue shift in Congo red assay and later expressing percentage inhibition in ThioflavinT assay as well. However, dose-dependent kinetics revealed that the antifibrillatory effects of drugs are more pronounced at low protein concentrations.Communicated by Ramaswamy H. Sarma.

Designing multi-target drugs for the treatment of major depressive disorder.

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. AREAS COVERED: In the current review, recent lead design and lead modification strategies have been covered. Important investigations reported in the last ten years (2013-2022) for the preclinical development of MTDLs (through synthetic medicinal chemistry and biological evaluation) for the treatment of MDD were discussed as case studies to focus on the recent design strategies. The discussions are categorized on the basis of pharmacological targets. Based on these important case studies, the challenges involved in different design strategies were discussed in detail. EXPERT OPINION: Even though large variations were observed in the selection of pharmacological targets, some potential biological targets (NMDA, melatonin receptors) are required to be explored extensively for the design of MTDLs. Similarly, apart from structure activity relationship (SAR), in silico techniques such as multitasking cheminformatic modeling, molecular dynamics simulation and virtual screening should be exploited to a greater extent.

Predicting the ecotoxicity of endocrine disruptive chemicals: Multitasking in silico approaches towards global models.

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. However, maximum external predictivity was achieved when a new multitasking consensus modeling approach was applied to these models. Additionally, the developed linear model provided means to investigate the determining factors for eliciting higher ecotoxicity by the EDCs towards different biological species, identifying several factors such as solvation, molecular mass and surface area as well as the number of specific molecular fragments (e.g.: aromatic hydroxy and aliphatic aldehyde). The resource to non-commercial open-access tools to develop the models is a useful step towards library screening to speed up regulatory decision on discovery of safe alternatives to reduce the hazards of EDCs.

Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD.

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. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.

In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus.

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 in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.

Synthesis, anticancer activity, SAR and binding mode of interaction studies of substituted pentanoic acids: part II.

Aim: Our previous results suggest that phenyl/naphthylacetyl pentanoic acid derivatives may exhibit dual MMP-2 and HDAC8 inhibitory activities and show effective cytotoxic properties. Methodology: Here, 13 new compounds (C1-C13) were synthesized and characterized. Along with these new compounds, 16 previously reported phenyl/napthylacetyl pentanoic acid derivatives (C14-C29) were biologically evaluated. Results: Compounds C6 and C27 showed good cytotoxicity against leukemia cell line Jurkat E6.1. The mechanisms of cytotoxicity of these compounds were confirmed by DNA deformation assay and reactive oxygen species assay. MMP-2 and HDAC8 expression assays suggested the dual inhibiting property of these two compounds. These findings were supported by results of molecular docking studies. In silico pharmacokinetic properties showed compounds C6 and C27 have high gastrointestinal absorption. Conclusion: This study highlights the action of phenyl/naphthylacetyl pentanoic acid derivatives as anticancer agents.

First multi-target QSAR model for predicting the cytotoxicity of acrylic acid-based dental monomers.

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. By contrast, a classification-based multi-target QSAR model was developed with 138 compounds, the cytotoxicity of which has been reported against 18 different cell lines. Both models were set up following rigorous validation protocols confirming their statistical significance and robustness. RESULTS: The performance of the linear mt-QSAR model, developed with various feature selection and post-selection similarity searching-based schemes, superseded that of all non-linear models produced with six machine learning methods by hyperparameter optimization. The final derived st-QSAR and mt-QSAR linear models are shown to be highly predictive, as well as revealing the crucial structural and physicochemical factors responsible for higher cytotoxicity of the dental monomers. SIGNIFICANCE: This study is the first attempt on unveiling the cytotoxicity of dental monomers over several cell lines by means of a single multi-target QSAR model. Further, such a model is ready to get widespread applicability in the screening of new monomers, judging from its almost accurate predictions over diverse experimental assay conditions.