G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA.

The study presents 'G4-QuadScreen', a user-friendly computational tool for identifying MTDLs against G4s. Also, it offers a few hit MTDLs based on in silico and in vitro approaches. Multi-tasking QSAR models were developed using linear discriminant analysis and random forest machine learning techniques for predicting the responses of interest (G4 interaction, G4 stabilization, G4 selectivity, and cytotoxicity) considering the variations in the experimental conditions (e.g., G4 sequences, endpoints, cell lines, buffers, and assays). A virtual screening with G4-QuadScreen and molecular docking using YASARA (AutoDock-Vina) was performed. G4 activities were confirmed via FRET melting, FID, and cell viability assays. Validation metrics demonstrated the high discriminatory power and robustness of the models (the accuracy of all models is ~>90% for the training sets and ~>80% for the external sets). The experimental evaluations showed that ten screened MTDLs have the capacity to selectively stabilize multiple G4s. Three screened MTDLs induced a strong inhibitory effect on various human cancer cell lines. This pioneering computational study serves a tool to accelerate the search for new leads against G4s, reducing false positive outcomes in the early stages of drug discovery. The G4-QuadScreen tool is accessible on the ChemoPredictionSuite website.

Prediction reliability of QSAR models: an overview of various validation tools.

The reliability of any quantitative structure-activity relationship (QSAR) model depends on multiple aspects such as the accuracy of the input dataset, selection of significant descriptors, the appropriate splitting process of the dataset, statistical tools used, and most notably on the measures of validation. Validation, the most crucial step in QSAR model development, confirms the reliability of the developed QSAR models and the acceptability of each step in the model development. The present review deals with various validation tools that involve multiple techniques that improve the model quality and robustness. The double cross-validation tool helps in building improved quality models using different combinations of the same training set in an inner cross-validation loop. This exhaustive method is also integrated for small datasets (< 40 compounds) in another tool, namely the small dataset modeler tool. The main aim of QSAR researchers is to improve prediction quality by lowering the prediction errors for the query compounds. 'Intelligent' selection of multiple models and consensus predictions integrated in the intelligent consensus predictor tool were found to be more externally predictive than individual models. Furthermore, another tool called Prediction Reliability Indicator was explained to understand the quality of predictions for a true external set. This tool uses a composite scoring technique to identify query compounds as 'good' or 'moderate' or 'bad' predictions. We have also discussed a quantitative read-across tool which predicts a chemical response based on the similarity with structural analogues. The discussed tools are freely available from https://dtclab.webs.com/software-tools or http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/ and https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home (for read-across).

New Workflow for QSAR Model Development from Small Data Sets: Small Dataset Curator and Small Dataset Modeler. Integration of Data Curation, Exhaustive Double Cross-Validation, and a Set of Optimal Model Selection Techniques.

Quantitative structure-activity relationship (QSAR) modeling is a well-known in silico technique with extensive applications in several major fields such as drug design, predictive toxicology, materials science, food science, etc. Handling small-sized datasets due to the lack of experimental data for specialized end points is a crucial task for the QSAR researcher. In the present study, we propose an integrated workflow/scheme capable of dealing with small dataset modeling that integrates dataset curation, "exhaustive" double cross-validation and a set of optimal model selection techniques including consensus predictions. We have developed two software tools, namely, Small Dataset Curator, version 1.0.0, and Small Dataset Modeler, version 1.0.0, to effortlessly execute the proposed workflow. These tools are freely available for download from https://dtclab.webs.com/software-tools . We have performed case studies employing seven diverse datasets to demonstrate the performance of the proposed scheme (including data curation) for small dataset QSAR modeling. The case studies also confirm the usability and stability of the developed software tools.

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.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach.

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, ß-secretase, monoamine oxidase B, glycogen synthase kinase 3ß, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals?

Quantitative structure-activity relationship (QSAR) models have long been used for making predictions and data gap filling in diverse fields including medicinal chemistry, predictive toxicology, environmental fate modeling, materials science, agricultural science, nanoscience, food science, and so forth. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more test set(s) for which the experimental response data are available. However, it is interesting to estimate the reliability of predictions when the model is applied to a completely new data set (true external set) even when the new data points are within applicability domain (AD) of the developed model. In the present study, we have categorized the quality of predictions for the test set or true external set into three groups (good, moderate, and bad) based on absolute prediction errors. Then, we have used three criteria [(a) mean absolute error of leave-one-out predictions for 10 most close training compounds for each query molecule; (b) AD in terms of similarity based on the standardization approach; and (c) proximity of the predicted value of the query compound to the mean training response] in different weighting schemes for making a composite score of predictions. It was found that using the most frequently appearing weighting scheme 0.5-0-0.5, the composite score-based categorization showed concordance with absolute prediction error-based categorization for more than 80% test data points while working with 5 different datasets with 15 models for each set derived in three different splitting techniques. These observations were also confirmed with true external sets for another four endpoints suggesting applicability of the scheme to judge the reliability of predictions for new datasets. The scheme has been implemented in a tool "Prediction Reliability Indicator" available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/, and the tool is presently valid for multiple linear regression models only.

CADD Modeling of Multi-Target Drugs Against Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that is described by multiple factors linked with the progression of the disease. The currently approved drugs in the market are not capable of curing AD; instead, they merely provide symptomatic relief. Development of multi-target directed ligands (MTDLs) is an emerging strategy for improving the quality of the treatment against complex diseases like AD. Polypharmacology is a branch of pharmaceutical sciences that deals with the MTDL development. In this mini-review, we have summarized and discussed different strategies that are reported in the literature to design MTDLs for AD. Further, we have discussed the role of different in silico techniques and online resources in computer-aided drug discovery (CADD), for designing or identifying MTDLs against AD.

Cytotoxic and Antioxidant Activity of a Set of Hetero Bicylic Methylthiadiazole Hydrazones: A Structure-Activity Study.

The current study highlights the in vitro antioxidant and antitumor activity of the previously-synthesized hydrazone derivatives against various free radicals and human cancer cell lines, respectively. The anticancer efficacies of the compound were tested by measuring cytotoxicity in cancer cell lines HeLa, A549, and non-cancerous NL20 cells. Compounds possessing electron-donor methoxy and methyl substitutions at the para position of the phenyl ring moiety showed a concentration dependent free radical scavenging effects. The free radical-scavenging potential of synthetic compounds 11 and 14 may have significant impact on the prevention of free radical-induced oxidative stress and carcinogenesis. The results from cytotoxicity and cell migration assay showed that the substitution of electron-withdrawing fluoro, chloro and bromo functional groups induced a significant (P< 0.001) loss of cell viability and inhibited the invasive potential of the human cancer cells. Additionally, these compounds showed significantly (P< 0.05) a less toxicity toward non-cancerous NL20 cells. Docking studies revealed interactions of compound 10 with p38α MAP kinase, which may be responsible of its anti-invasive and anti-proliferative effects.

In vitro evaluation and in silico screening of synthetic acetylcholinesterase inhibitors bearing functionalized piperidine pharmacophores.

A series of densely functionalized piperidine (FP) scaffolds was synthesized following a diastereoselective one-pot multicomponent protocol under eco-friendly conditions. The FPs were evaluated in vitro for their acetylcholinesterase (AChE) inhibitory activity, and in silico studies for all the target compounds were carried out using pharmacophore mapping, molecular docking and quantitative structure-activity relationship (QSAR) analysis in order to understand the structural features required for interaction with the AChE enzyme and the key active site residues involved in the intermolecular interactions. Halogenation, nitration or 3,4-methylenedixoxy-substitution at the phenyl ring attached to the 2- and 6-positions of 1,2,5,6-tetrahydropyridine nucleus in compounds 14-17, 19, 20, 24 and 26 greatly enhanced the AChE inhibitory activity. The docking analysis demonstrated that the inhibitors are well-fitted in the active sites. The in silico studies enlighten the future course of studies in modifying the scaffolds for better therapeutic efficacy against the deadly Alzheimer's disease.

Exploring structural requirements of imaging agents against Aß plaques in Alzheimer's disease: a QSAR approach.

Exploring molecular imaging agents against the beta amyloid (Aß) plaques for an early detection of Alzheimer's disease (AD) is one of the emerging research areas in medicinal chemistry. In the present in-silico study, a congeneric series of 44 imaging agents, including 17 positron emission tomography (PET) and 27 single photon emission computed tomography (SPECT) imaging agents, was utilized to understand the structural features required for having essential binding affinity against Aß plaques. Here, 2D-quantitative structure-activity relationship (2D-QSAR) and group-based QSAR (G-QSAR) models have been developed using genetic function approximation (GFA) and validated using various statistical metrics. Both the models showed satisfactory performance signifying the reliability and robustness of the developed QSAR models. The vital information gained from both the QSAR models will be useful in developing new PET and SPECT imaging agents and also in predicting their binding affinity against Aß plaques. The results of this study would be important in view of the widespread clinical applicability of the SPECT imaging agents, especially in the developing countries. In this study, we have also designed some imaging agents based on the information provided by the models. Some of these designed compounds were predicted to be similar to or more active than the most active imaging agents present in the original dataset.

Advances in quantitative structure-activity relationship models of anti-Alzheimer's agents.

INTRODUCTION: Alzheimer's disease (AD) is one of the lethal diseases, mainly affecting older people. The unclear root cause and involvement of various enzymes in the pathological conditions confirm the complexity of the disease. Quantitative structure-activity relationship (QSAR) techniques are of great significance in the design of drugs against AD. AREAS COVERED: In the present review, the authors provide a basic background about AD and QSAR techniques. Furthermore, they review the various QSAR studies reported against various targets of AD. The information provided for each QSAR study includes chemical scaffold and target enzyme under study, applied QSAR technique and outcomes of the respective study. EXPERT OPINION: In silico techniques like QSAR hold great potential in designing leads against a complex disease like AD. In combination with other in silico techniques, QSAR can provide more useful and rational insight to facilitate the discovery of novel compounds. Only few QSAR studies on imaging agents have been reported; hence, more QSAR studies are recommended to explore the biomarker or imaging agents for improving diagnosis. Again, for proper symptomatic treatment, multi-target drugs acting on more than one target are required. Hence, more multi-target QSAR studies are recommended in future to achieve this goal.

Pharmacophore mapping-based virtual screening followed by molecular docking studies in search of potential acetylcholinesterase inhibitors as anti-Alzheimer's agents.

Alzheimer's disease (AD) is turning out to be one of the lethal diseases in older people. Acetylcholinesterase (AChE) is a crucial target in designing of drugs against AD. The present in silico study was carried out to explore natural compounds as potential AChE inhibitors. Virtual screening, via drug-like ADMET filter, best pharmacophore model and molecular docking analyses, has been utilized to identify putative novel AChE inhibitors. The InterBioScreen's Natural Compound (NC) database was first filtered by applying drug-like ADMET properties and then with the pharmacophore-based virtual screening followed by molecular docking analyses. Based on docking score, interaction patterns and calculated activity, the final hits were selected and these consist of coumarin and non-coumarin classes of compounds. Few hits were found to have been already reported for their AChE inhibitory activity in different literatures confirming reliability of our pharmacophore model. The remaining hits are suggested to be potential AChE inhibitors for AD.

3D-QSAR and molecular docking analysis of biphenyl amide derivatives as p38α mitogen-activated protein kinase inhibitors.

The p38α mitogen-activated protein (MAP) kinase plays a vital role in treating many inflammatory diseases such as rheumatoid arthritis, inflammatory bowel disease, Crohn's disease and psoriasis. Herein, we have performed 3D-QSAR and molecular docking analysis on a novel series of biphenyl amides to design potent p38 MAP kinase inhibitors. This study correlates the p38 MAP kinase inhibitory activities of 80 biphenyl amide derivatives to several stereochemical parameters representing steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor fields. The resulting model from CoMFA and CoMSIA exhibited excellent [Formula: see text] values of 0.979 and 0.942, and [Formula: see text] values of 0.766 and 0.748, respectively. CoMFA predicted [Formula: see text] of 0.987 and CoMSIA predicted [Formula: see text] of 0.761 showed that the predicted values were in good agreement with experimental values. Glide (5.5) program gave the path for binding mode exploration between the inhibitors and p38α MAP kinase. We have accordingly designed novel p38α MAP kinase inhibitors by utilizing LeapFrog and predicted with excellent activity in the developed models.