scholarly journals Rodent Carcinogenicity Dataset

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Natalja Fjodorova ◽  
Marjana Novič
Keyword(s):  
Molecular Structure ◽  
Quantitative Structure Activity Relationship ◽  
Future Research ◽  
Qsar Modeling ◽  
Carcinogenic Potency ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
Funded Project ◽  
Rodent Carcinogenicity

The rodent carcinogenicity dataset was compiled from the Carcinogenic Potency Database (CPDBAS) and was applied for the classification of quantitative structure-activity relationship (QSAR) models for the prediction of carcinogenicity based on the counter-propagation artificial neural network (CP ANN) algorithm. The models were developed within EU-funded project CAESAR for regulatory use. The dataset contains the following information: common information about chemicals (ID, chemical name, and their CASRN), molecular structure information (SDF files and SMILES), and carcinogenic (toxicological) properties information: carcinogenic potency (TD50_Rat_mg; carcinogen/noncarcinogen) and structural alert (SA) for carcinogenicity based on mechanistic data. Molecular structure information was used to get chemometrics information to calculate molecular descriptors (254 MDL and 784 Dragon descriptors), which were further used in predictive QSAR modeling. The dataset presented in the paper can be used in future research in oncology, ecology, or chemicals' risk assessment.

scholarly journals Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules

2019 ◽  
Vol 20 (9) ◽  
pp. 2311 ◽  
Author(s):  
Giuseppe Floresta ◽  
Antonio Rescifina ◽  
Vincenzo Abbate
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Mu Opioid Receptor ◽  
Scaffold Hopping ◽  
Consensus Model ◽  
Qsar Modeling ◽  
Mu Opioid ◽  
Structure Activity ◽  
Opioid Binding ◽  
Qsar Models

Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu-opioid receptor (OR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for their predictive and descriptive capabilities. The three different approaches were then combined to produce a consensus model and were exploited to explore the chemical landscape of 3000 fentanyl-like structures, generated by a theoretical scaffold-hopping approach. The findings of this study should facilitate the identification and classification of new OR ligands with fentanyl-like structures.

QSAR modeling for reaction rate constants of eaq− with diverse organic compounds in water

2020 ◽  
Vol 6 (7) ◽  
pp. 1931-1938
Author(s):  
Shanshan Zheng ◽  
Chao Li ◽  
Gaoliang Wei
Keyword(s):  
Organic Compounds ◽  
Reaction Rate ◽  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Reaction Rate Constants ◽  
Structure Activity ◽  
Qsar Models ◽  
The Impact

Two quantitative structure–activity relationship (QSAR) models to predict keaq− of diverse organic compounds were developed and the impact of molecular structural features on eaq− reactivity was investigated.

scholarly journals A QSAR Modeling on Aurone Derivatives as Antimalarial Agents

2020 ◽  
Vol 32 (11) ◽  
pp. 2839-2845
Author(s):  
R. Hadanau
Keyword(s):  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Multilinear Regression ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Antimalarial Agents ◽  
Inhibition Concentration ◽  
Qsar Models ◽  
Semi Empirical ◽  
Statistical Criteria

A quantitative structure activity relationship (QSAR) analysis was performed on several compound and aurone derivatives (1-16) and 17-21 compounds were used as internal and external tests, respectively. Studies have investigated aurone derivatives; however, for aurone compounds, QSAR analysis has not been conducted. The semi-empirical PM3 method of HyperChem for Windows 8.0 was used to optimise the aurone derivative structures to acquire descriptors. For 15 influential descriptors, the multilinear regression MLR analysis was conducted by employing the backward method, and four new QSAR models were obtained. According to statistical criteria, model 2 was the optimum QSAR model for predicting the inhibition concentration (IC50) theoretical value against novel aurone derivatives. The modelling of 40 (22-61) aurone compounds was achieved. Six novel compounds (54, 55, 58, 59, 60, and 61) were synthesized in a laboratory because the IC50 of these compounds was lower than that of chloroquine (IC50 = 0.14 μM).

An Analysis of QSAR Research Based on Machine Learning Concepts

2020 ◽  
Vol 17 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Mehrnoush Barani Shirzad
Keyword(s):  
Machine Learning ◽  
Learning Strategies ◽  
Biological Activities ◽  
Analytical Framework ◽  
Quantitative Structure Activity Relationship ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Qsar Modeling ◽  
Chemical Structures

: Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called ‘ML-QSAR‘. This framework has been designed for future research to: a)facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

scholarly journals QSAR Models for Predicting Five Levels of Cellular Accumulation of Lysosomotropic Macrocycles

2019 ◽  
Vol 20 (23) ◽  
pp. 5938 ◽  
Author(s):  
Ulf Norinder ◽  
Vesna Munic Kos
Keyword(s):  
Molecular Structure ◽  
High Volume ◽  
Pharmacokinetic Profile ◽  
Quantitative Structure Activity Relationship ◽  
Volume Of Distribution ◽  
Cellular Accumulation ◽  
Large Size ◽  
Qsar Models ◽  
First Time ◽  
Very High

Drugs that accumulate in lysosomes reach very high tissue concentrations, which is evident in the high volume of distribution and often lower clearance of these compounds. Such a pharmacokinetic profile is beneficial for indications where high tissue penetration and a less frequent dosing regime is required. Here, we show how the level of lysosomotropic accumulation in cells can be predicted solely from molecular structure. To develop quantitative structure–activity relationship (QSAR) models, we used cellular accumulation data for 69 lysosomotropic macrocycles, the pharmaceutical class for which this type of prediction model is extremely valuable due to the importance of cellular accumulation for their anti-infective and anti-inflammatory applications as well as due to the fact that they are extremely difficult to model by computational methods because of their large size (Mw > 500). For the first time, we show that five levels of intracellular lysosomotropic accumulation (as measured by liquid chromatography coupled to tandem mass spectrometry—LC-MS/MS), from low/no to extremely high, can be predicted with 60% balanced accuracy solely from the compound’s structure. Although largely built on macrocycles, the eight non-macrocyclic compounds that were added to the set were found to be well incorporated by the models, indicating their possible broader application. By uncovering the link between the molecular structure and cellular accumulation as the key process in tissue distribution of lysosomotropic compounds, these models are applicable for directing the drug discovery process and prioritizing the compounds for synthesis with fine-tuned accumulation properties, according to the desired pharmacokinetic profile.

Predicting the adsorption of organic pollutants on boron nitride nanosheets via in silico techniques: DFT computations and QSAR modeling

2021 ◽  
Author(s):  
Ya Wang ◽  
Weihao Tang ◽  
Yue Peng ◽  
Zhongfang Chen ◽  
Jingwen Chen ◽  
...  
Keyword(s):  
Boron Nitride ◽  
Organic Pollutants ◽  
In Silico ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Boron Nitride Nanosheets ◽  
Structure Activity ◽  
Aqueous Environments ◽  
Qsar Models

Four quantitative structure–activity relationship (QSAR) models were developed for predicting the log K values of organic pollutants adsorbed onto boron nitride nanosheets in gaseous and aqueous environments.

Use of QSAR Modeling to Predict the Carcinogenicity of Color Additives

2013 ◽  
Author(s):  
Ronald Brown ◽  
Shannon White ◽  
Jennifer Goode ◽  
Prachi Pradeep ◽  
Stephen Merrill
Keyword(s):  
Medical Devices ◽  
Safety Evaluation ◽  
Toxicity Testing ◽  
Quantitative Structure Activity Relationship ◽  
Draft Guidance ◽  
Qsar Modeling ◽  
Wide Range ◽  
Qsar Models ◽  
Us Fda ◽  
Radiological Health

Patients may be exposed to potentially carcinogenic color additives released from polymers used to manufacture medical devices; therefore, the need exists to adequately assess the safety of these compounds. The US FDA Center for Devices and Radiological Health (CDRH) recently issued draft guidance that, when final, will include FDA’s recommendations for the safety evaluation of color additives and other potentially toxic chemical entities that may be released from device materials. Specifically, the draft guidance outlines an approach that calls for evaluating the potential for the color additive to be released from the device in concert with available toxicity information about the additive to determine what types of toxicity information, if any, are necessary. However, when toxicity data are not available from the literature for the compounds of interest, a scientific rationale can sometimes be provided for omission of these tests. Although the FDA has issued draft guidance on this topic, the Agency continues to explore alternative approaches to understand when additional toxicity testing is needed to assure the safety of medical devices that contain color additives. An emerging approach that may be useful for determining the need for further testing of compounds released from device materials is Quantitative Structure Activity Relationship (QSAR) modeling. In this paper, we have shown how three publically available QSAR models (OpenTox/Lazar, Toxtree, and the OECD Toolbox) are able to successfully predict the carcinogenic potential of a set of color additives with a wide range of structures. As a result, this computational modeling approach may serve as a useful tool for determining the need to conduct carcinogenicity testing of color additives intended for use in medical devices.

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