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21 results for “QSAR”
Supplementary Files for Pore-Level QSAR on Aquaporins
<p>These files include the output files from PoreWalker server used in the pore-level QSAR performed. An ad-hoc Python script prepared to perform all regression, statistical and y-scrambling analyses is also provided.</p>
Predictive nano-QSAR modeling of the cytotoxicity using epithelial cells obtained from Chinese hamster ovary (CHO-K1 cell line) for hybrid TiO2-based nanomaterials
<p>Results obtained from developed model indicated that the cytotoxicity of hybrid TiO2-based nanomaterials is related to additive electronegativity (χmix) of studied nanomaterials that are indirectly related to the electron generation and ROS formation. ROS production is the most common toxicity cause as discussed in the literature in the case of nanoparticles. The high efficiency of surface modified TiO2-based semiconductors can be attributed to the involvement of TiO2 band gap (Eg) excitation and absence of noble metals at the TiO2 surface. It can be expected that noble metals (i.e. Pd/Pt) may trap holes (h+), at the same time photo-generated electrons can be then transferred from the valence band to the conduction band of TiO2 and to its surface where redox processes were initiated. Thus, observed reduction of the electron–hole pair recombination influences the reactive oxygen species (ROS) formation and the photocatalytic redox process initiation.</p> <p>Since the electronegativity was positively correlated with the cytotoxicity it can be expected that some ions are released from the TiO2 surface easier than others.</p>
Consensus QSAR models estimating acute aquatic toxicity for three trophic levels organisms: Algae, Daphnia and Fish
<p>We report new consensus models estimating acute toxicity for algae, daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSE<sub>ext</sub> = 0.56 – 0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/<em>ColorAtom</em> utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracies and data coverage.</p> <p>Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending applicability domain and relevance for application in an industrial context. Generate models and collected public data are made freely available.</p> <p><strong>Available fields in the SDF file:</strong></p> <ul> <li>SMILES_Canonical: canonical SMILES code</li> <li>DB: source of the data, "Litterature set" means that the data is originated from an article (see the companion article of the dataset for details).</li> <li>endpoint: organism for which endpoint is available</li> <li>CASRN: CAS registration number</li> <li>98-81-7</li> <li>pEC50 - DAPHNIA: Daphnia, mortality, which is evaluated by the immobilization of the invertebrate is recorded at 48 hours and expressed as the log median effective concentration (pEC50)</li> <li>mg/L - DAPHNIA: Daphnia, mortality, which is evaluated by the immobilization of the invertebrate is recorded at 48 hours and expressed as the median effective concentration (EC50)</li> <li>pLC50 - FISH: Fish, the log median lethal concentration measured at 96 hours is considered (pLC50)</li> <li>mg/L - FISH: Fish, the log median lethal concentration measured at 96 hours is considered (LC50)</li> <li>pEC50 - ALGA: Algae, the purpose is to determine the substance’s growth inhibition effect, expressed as the log median effective concentration (pEC50) measured at 72 hours</li> <li>mg/L - ALGA: Algae, the purpose is to determine the substance’s growth inhibition effect, expressed as the median effective concentration (EC50) measured at 72 hours</li> </ul>
Morphing libraries, QSAR models, and compounds predicted to be active on the Glucocorticoid receptor (GR)
<p>This repository contains datasets and files related to the computational drug discovery project of the chemical space exploration of the Glucocorticoid receptor. The accompanying Python code is freely available in the GitHub repository (<a title="https://github.com/Iagea/GRML_analyses" href="https://github.com/Iagea/GRML_analyses" target="_blank" rel="noreferrer noopener">https://github.com/Iagea/GRML_analyses</a>).</p> <p><strong>Morphing Libraries:</strong></p> <ul> <li><strong>GRML_library.csv:</strong> The GRML library is the collection of 999,015 virtual compounds generated by Molpher [1-2] starting from GR ligands with unique Bemis-Murcko scaffolds collected from the ChEMBL17 and IMG libraries.</li> <li><strong>RML_library.csv:</strong> The RML library is the collection of 1,346,310 virtual compounds generated by Molpher starting from compounds with unique Bemis-Murcko scaffolds randomly selected from the ZINC database.</li> </ul> <p><strong>IMG library:</strong></p> <ul> <li><strong>IMG_non_proprietary.csv</strong>: The non-proprietary IMG library subset containing 12,956 compounds and their corresponding B-scores from the primary screen.</li> </ul> <p><strong>Molpher inputs:</strong></p> <ul> <li><strong>GR_inputs.csv</strong>: The GR inputs are the ligands used to create the GRML library, 204 compounds from ChEMBL17 (95 compounds) and the non-proprietary dataset from IMG (109 compounds).</li> <li><strong>Random_inputs.csv</strong>: The random inputs are 249 random ZINC compounds used to create the Random library.</li> </ul> <p><strong>Model's training sets:</strong></p> <ul> <li><strong>Model33_training_set.csv</strong>: Random forest classification model training set, it includes 865 compounds; known GR actives and inactives from ChEMBL33 (738 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>Model17_training_set.csv</strong>: Random forest classification model training set, it includes 601 compounds; known GR actives and inactives from ChEMBL17 (474 compounds) and non-proprietary active ligands from the IMG library (127 compounds).</li> <li><strong>RFR_training_set.csv</strong>: Random forest regression model training set, it includes 89 compounds; known GR actives and inactives from ChEMBL33 that fit into the GR pharmacophore with the four features we describe in our paper.</li> </ul> <p><strong>Models:</strong></p> <ul> <li><strong>Model33.pkl:</strong> Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL33 and IMG libraries.</li> <li><strong>Model17.pkl</strong>: Python pickle file containing the trained Random forest classification models used along with Mondrian cross-conformal prediction to classify GR actives/inactives. This model was trained with ChEMBL17 and IMG libraries.</li> <li><strong>RFR_models.pkl</strong><em>: </em>Python pickle file containing the 100 trained random forest regression models used to rank the proposed active morphs. These models were trained with the RFR_training_set.csv.</li> </ul> <p><strong>Active predicted morphs:</strong></p> <ul> <li><strong>all_morphs_actives</strong><em><strong>_</strong></em><strong>predicted.xlsx:</strong> An Excel spreadsheet containing two sheets. 1) All 22,524 GRML active predicted morphs. 2) All 4,341 RML active predicted morphs. The QED, NIBR Severity Score, and Molskill Score are given for each morph.</li> </ul> <p><strong>Proposed GR active ligands:</strong></p> <ul> <li><strong>designed_ligands.xlsx</strong>: An Excel spreadsheet containing two sheets. 1) All 54 designed GR ligands with their QED, NIBR severity score, MolSkill score, consensus ranking from the 100 RFR models, and the result of the manual annotation and remarks, if available. 2) The structure of the 54 ligands based on their manual annotation and presence or not in ChEMBL33 database.</li> </ul> <p>Researchers and professionals in the field of drug discovery and cheminformatics may find these resources useful for further analysis and investigations.</p> <p><strong>Bibliography</strong></p> <p>[1] Hoksza, D., Škoda, P., Voršilák, M. <em>et al.</em> Molpher: a software framework for systematic chemical space exploration. <em>J Cheminform</em> <strong>6</strong>, 7 (2014). https://doi.org/10.1186/1758-2946-6-7</p> <p>[2] <a href="https://github.com/lich-uct/molpher-lib">https://github.com/lich-uct/molpher-lib</a></p>
In silico design, docking simulation, and ANN-QSAR model for predicting the anticoagulant activity of thiourea isosteviol compounds as FXa inhibitors
<p>The present work combined molecular modeling and docking approach for searching and designing novel thiourea isosteviol-based compounds as potential FXa inhibitors. Elaborated regression model establishes the relationships between experimentally determined anticoagulant activity and molecular descriptors and enables the prediction of FXa inhibitory activity for novel compounds. The obtained results proved that the Artificial Neural Network algorithm facilitates the search for the most promising isosteviol derivatives incorporating thiourea fragments as FXa inhibitors. Moreover, docking simulation confirms the prominent binding of the newly in silico designed molecules with the active sites of the protein, which may be the lead molecules and can be further optimized for the efficient pharmacodynamic and pharmacokinetic profiles. The enclosed files are representations of molecular structures of thiourea isosteviol compounds with experimentally tested FXa inhibitory activity (i20-i39) geometrically optimized in hyperchem, newly in silico designed thiourea isosteviol compounds geometrically optimized in hyperchem (e1-e11), one file contains molecular descriptors for optimized structures calculated in Dragon and there is also a code for ANN QSAR model for predicting activity of novel thiourea isosteviol compounds. </p>
Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites
<p>This study presents applications of advancements in QSAR modelling for predicting nitric oxide (NO) inhibitors and anti-inflammatory metabolites from the <em>Aspergillus</em> genus. Inflammation-related diseases remain a pressing concern, necessitating the identification of effective anti-inflammatory compounds. Using decision trees, the Ranker method, and CorrelationAtrributeEval as a base classifier for attribute selection together with Rotation Forest and Adaboost as enhancers, we explored their potential with different classifiers including Artificial Neural Networks and J48 Trees. The proposed QSAR models employed an ensemble approach with Rotation Forest and Adaboost.M1, applying an automated KNIME workflow. Seven molecular descriptors were selected and trained on a comprehensive dataset of diverse anti-inflammatory <em>Aspergillus</em> specialised metabolites. Results showed that the Rotation Forest-enhanced version outperformed other models, capturing complex structure-activity relationships and improving predictive performance. Chemical characteristics of electrotopological state, topological distances, and functional groups including secondary amides and alcohols contribute to important anti-inflammatory effects. The developed QSAR model showed good predictive performance for anti-inflammatory <em>Aspergillus</em> metabolites, focusing on their NO inhibitory activity. These results can contribute to the discovery of novel anti-inflammatory drugs based on computational techniques.</p> <p> </p>
Predicting zeta potential of liposomes from their structure: A nano-QSAR model for DOPE, DC-Chol, DOTAP, and EPC formulations
<p>Data set for publication: "<em>Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations.</em>"; Computational and Structural Biotechnology Journal 25 (2024) 3–8; https://doi.org/10.1016/j.csbj.2024.01.012 </p>
QSAR data of indigo, indirubin, and isoindigotin, including tautomers
<p>Quantitative structure-activity relationship (QSAR) data of indigo and the isomers indirubin and isoindigotin, as well as their tautomers.</p>
QSAR dataset for anti melanogenic agents - tyrosinase inhibition
<p>Kindly cite if you are using the dataset for predicting the tyrosinase inhibitory activity for your compounds.</p> <p>We generated a validated QSAR model with a dataset consisting of 69 thio-semicarbazone derivatives to elucidate the physicochemical properties of compounds essential for tyrosinase inhibition and to identify novel lead molecules with enhanced tyrosinase inhibitory activity and bioavailability.</p> <p> </p> <p>Thanks,</p> <p>Authors</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Supplementary material 1 from: Habash M, Alshakhshir S, Awwad S, Abu-Samak M (2023) The discovery of potential tumor necrosis factor alpha converting enzyme inhibitors via implementation of K Nearest Neighbor QSAR analysis. Pharmacia 70(2): 247-261. https://doi.org/10.3897/pharmacia.70.e96423
The discovery of potential tumor necrosis factor alpha converting enzyme inhibitors via implementation of K Nearest Neighbor QSAR analysis
QSAR data of 2,9-dimethylquinacridone tautomers
<p>Quantitative structure-activity relationship (QSAR) data of 2,9-dimethylquinacridone tautomers.</p>
Identification of novel NLRP3 Inhibitors a comprehensive approach using 2D-QSAR, molecular docking, molecular dynamics simulation and drug-likeness evaluation
<p>This dataset encapsulates the comprehensive outputs derived from molecular docking and molecular dynamics (MD) simulation studies conducted to investigate the binding affinities, interactions, and dynamic behaviors of selected ligands with NLRP3.</p>
Caramel: an web-based QSAR tool for melanoma drug discovery - Bambu Models
Open the record for dataset details and reuse information.
Figure 3 from: Drapak І, Zimenkovsky B, Perekhoda L, Yeromina H, Lipakova K, Demchuk I, Rakhimova M (2019) QSAR-analysis of 1-[2-(R-phenylimino)-4-methyl-3-(3-[morpholine-4-yl]propyl)-2,3-dihydro-1,3-thiazol-5-yl]ethane-1-one's derivatives as potential antioxidants. Pharmacia 66(1): 33-40. https://doi.org/10.3897/pharmacia.66.e35083
Figure 3 The dependence of the observed and predicted antioxidant activity for QSAR-models 5(a) and 15(b) for compounds of the training sample.
Figure 1 from: Drapak І, Zimenkovsky B, Perekhoda L, Yeromina H, Lipakova K, Demchuk I, Rakhimova M (2019) QSAR-analysis of 1-[2-(R-phenylimino)-4-methyl-3-(3-[morpholine-4-yl]propyl)-2,3-dihydro-1,3-thiazol-5-yl]ethane-1-one's derivatives as potential antioxidants. Pharmacia 66(1): 33-40. https://doi.org/10.3897/pharmacia.66.e35083
Figure 1 Structure of investigated compounds. R= a) H, b) 2-CH3, c) 2,3-(CH3)2, d) 2,4-(CH3)2, e) 2,6-(CH3)2, f) 3,4-(CH3)2, g) 3,5-(CH3)2, h) 2-OCH3, i) 3-OCH3, j) 4-OCH3, k) 2-Cl, l) 3-Cl, m) 4-Cl.
Figure 2 from: Drapak І, Zimenkovsky B, Perekhoda L, Yeromina H, Lipakova K, Demchuk I, Rakhimova M (2019) QSAR-analysis of 1-[2-(R-phenylimino)-4-methyl-3-(3-[morpholine-4-yl]propyl)-2,3-dihydro-1,3-thiazol-5-yl]ethane-1-one's derivatives as potential antioxidants. Pharmacia 66(1): 33-40. https://doi.org/10.3897/pharmacia.66.e35083
Figure 2 The dependence of observed and predicted antioxidant activities for QSAR-models 2(a) and 3(b) for compounds of the training sample.
A dataset for establishing a machine learning-based QSAR model to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library
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Bambu: a simple tool to generate QSAR models from bioassays data
<p>Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity, based on its structure. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications on different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets. </p> <p>The tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds.</p> <p>Based on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The tool is available through the URL <a href="http://caramel.ufpel.edu.br">http://caramel.ufpel.edu.br</a>.</p> <p> Bambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.</p>
Quantitative Structure-Activity Relationship (QSAR)-enabled in vitro Genotoxicity assessments of Titanium Dioxide Nanomaterials.
<p>This dataset provides information on the physicochemical properties of titanium dioxide (TiO<sub>2</sub>) nanomaterial, experimental conditions, characterization methods, as well as the different assays evaluating genotoxicity.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.