scholarly journals QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling

2020 ◽  
Author(s):  
M. Natália Dias Soeiro Cordeiro ◽  
Amit Kumar Halder
Keyword(s):  
Open Source ◽  
Linear Models ◽  
Moving Average ◽  
Biological Data ◽  
Statistical Parameters ◽  
Computational Tools ◽  
Qsar Modelling ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships ◽  
Set Up

Abstract 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 tools 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 multi-target 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. We have developed QSAR-Co-X, an open source python−based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters along with graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, three case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

scholarly journals QSAR-Co-X: an open source toolkit for multitarget QSAR modelling

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amit Kumar Halder ◽  
M. Natália Dias Soeiro Cordeiro
Keyword(s):  
Open Source ◽  
Linear Models ◽  
Moving Average ◽  
Biological Data ◽  
Statistical Parameters ◽  
Computational Tools ◽  
Qsar Modelling ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships ◽  
Set Up

AbstractQuantitative 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. We have developed QSAR-Co-X, an open source python–based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters and graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, four case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Linear and nonlinear QSAR models of acute intravenous toxicity to mice for organic chemicals

2012 ◽  
Vol 58 (4) ◽  
pp. 357-371 ◽  
Author(s):  
O.A. Raevsky ◽  
E.A. Liplavskaya ◽  
A.V. Yarkov ◽  
O.E. Raevskaya ◽  
A.P. Worth
Keyword(s):  
Functional Group ◽  
Linear Models ◽  
Nonlinear Models ◽  
Statistical Characteristics ◽  
Organic Chemicals ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Qsar Models ◽  
Linear And Nonlinear ◽  
Small Clusters

QSAR analysis of acute intravenous toxicity to mice for 68 monofunctional chemicals is presented. There compounds represents seven classes of organic chemicals: hydrocarbons (6 chemicals), alcohols (13), amides (22), amines (12), ethers (5), ketones (7), nitriles (3). Preliminary consideration of data for these chemicals showed that it is necessary to consider not only linear toxicity - descriptors relationships, but also nonlinear models. The linear and nonlinear QSAR models were considered for each from indicated classes of organic chemicals. Analogical models were constructed for whole subset of monofunctional chemicals. The statistical parameters and robustness of nonlinear models are essential better then statistics of linear models. Replacing a lipophilicity descriptor with molecular polarizability and H-bond ability in nonlinear models permits also to improve statistical characteristics. Clearly, if relationships between the intravenous toxicity of compounds bearing only a single functional group and lipophilicity are nonlinear, then similar relationships must be considered with compounds containing more than one functional group. To check up this idea whole set of small clusters containing structure relative compounds with few functional groups was examined from position of linear and nonlinear relationships between toxicity and lipophilicity. It was estimated in most causes advantages of nonlinear models.

DFT-Based QSAR Modelling of Inhibitory Activity of Coumarins and Sulfocoumarins on Carbonic Anhydrase (CA) Isoforms (CA I and CA II)

2019 ◽  
Vol 15 (3) ◽  
pp. 243-251 ◽  
Author(s):  
Erol Eroglu
Keyword(s):  
Molecular Descriptors ◽  
Binding Free Energy ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Parameters ◽  
Final Model ◽  
Qsar Modelling ◽  
Qsar Models ◽  
Orbital Electron ◽  
Test Sets ◽  
Kinetics Of

<P>Objective: We present three robust, validated and statistically significant quantitative structure-activity relationship (QSAR) models, which deal with the calculated molecular descriptors and experimental inhibition constant (Ki) of 42 coumarin and sulfocoumarin derivatives measured against CA I and II isoforms. </P><P> Methods: The compounds were subjected to DFT calculations in order to obtain quantum chemical molecular descriptors. Multiple linear regression algorithms were applied to construct QSAR models. Separation of the compounds into training and test sets was accomplished using Kennard-Stone algorithm. Leverage approach was applied to determine Applicability Domain (AD) of the obtained models. </P><P> Results: Three models were developed. The first model, CAI_model1 comprises 30/11 training/test compounds with the statistical parameters of R2=0.85, Q2=0.77, F=27.57, R2 (test) =0.72. The second one, CAII_model2 comprises 30/12 training/test compounds with the statistical parameters of R2=0.86, Q2=0.78, F=30.27, R2 (test) =0.85. The final model, &#916;pKi_model3 consists of 25/3 training/ test compounds with the statistical parameters of R2=0.78, Q2=0.62, F=13.80 and R2(test) =0.99. </P><P> Conclusion: Interpretation of reactivity-related descriptors such as HOMO-1 and LUMO energies and visual inspection of their maps of orbital electron density leads to a conclusion that the binding free energy of the entire binding process may be modulated by the kinetics of the hydrolyzing step of coumarins.</P>

scholarly journals The SoftWipe tool and benchmark for assessing coding standards adherence of scientific software

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adrian Zapletal ◽  
Dimitri Höhler ◽  
Carsten Sinz ◽  
Alexandros Stamatakis
Keyword(s):  
Open Source ◽  
Review Process ◽  
Selection Process ◽  
Software Selection ◽  
Scientific Software ◽  
Worst Case ◽  
Computational Tools ◽  
Research Areas ◽  
Coding Standards ◽  
Scientific Results

AbstractScientific software from all areas of scientific research is pivotal to obtaining novel insights. Yet the coding standards adherence of scientific software is rarely assessed, even though it might lead to incorrect scientific results in the worst case. Therefore, we have developed an open source tool and benchmark called , that provides a relative software coding standards adherence ranking of 48 computational tools from diverse research areas. can be used in the review process of software papers and to inform the scientific software selection process.

scholarly journals Short-term adoption rates for a web-based portal within the intranet of a hospital information system

2019 ◽  
Vol 26 (1) ◽  
pp. e100004 ◽  
Author(s):  
Athanasios Kotoulas ◽  
Ioannis Stratis ◽  
Theodoros Goumenidis ◽  
George Lambrou ◽  
Dimitrios - Dionysios Koutsouris
Keyword(s):  
Open Source ◽  
Resistance To Change ◽  
Content Management ◽  
Healthcare Management ◽  
Short Term ◽  
Web Based ◽  
Average Growth ◽  
Greek Hospital ◽  
Set Up ◽  
Free Open Source

ObjectiveAn intranet portal that combines cost-free, open-source software technology with easy set-up features can be beneficial for daily hospital processes. We describe the short-term adoption rates of a costless content management system (CMS) in the intranet of a tertiary Greek hospital.DesignDashboard statistics of our CMS platform were the implementation assessment of our system.ResultsIn a period of 10 months of running the software, the results indicate the employees overcame ‘Resistance to Change’ status. The average growth rate of end users who exploit the portal services is calculated as 2.73 every 3.3 months.ConclusionWe found our intranet web-based portal to be acceptable and helpful so far. Exploitation of an open-source CMS within the hospital intranet can influence healthcare management and the employees’ way of working as well.

4D-QSAR models applied to the study of TGF- β1 receptor inhibitors

2021 ◽  
Vol 21 ◽  
Author(s):  
Tamiris Maria de Assis ◽  
Teodorico Castro Ramalho ◽  
Elaine Fontes Ferreira da Cunha
Keyword(s):  
External Validation ◽  
Qsar Model ◽  
Dynamics Simulation ◽  
Conformational Ensemble ◽  
Pyrimidine Derivatives ◽  
Statistical Parameters ◽  
Data Set ◽  
Mesenchymal Transition ◽  
Qsar Models ◽  
Tgf Β1

Background: The quantitative structure-activity relationship is an analysis method that can be applied for designing new molecules. In 1997, Hopfinger and coworkers developed the 4D-QSAR methodology aiming to eliminate the question of which conformation to use in a QSAR study. In this work, the 4D-QSAR methodology was used to quantitatively determine the influence of structural descriptors on the activity of aryl pyrimidine derivatives as inhibitors of the TGF-β1 receptor. The members of the TGF-β subfamily are interesting molecular targets, since they play an important function in the growth and development of cell cellular including proliferation, apoptosis, differentiation, epithelial-mesenchymal transition (EMT), and migration. In late stages, TGF-β exerts tumor-promoting effects, increasing tumor invasiveness, and metastasis. Therefore, TGF-β is an attractive target for cancer therapy. Objective: The major goal of the current research is to develop 4D-QSAR models aiming to propose new structures of aryl pyrimidine derivatives. Materials and Methods: Molecular dynamics simulation was carried out to generate the conformational ensemble profile of a data set with aryl pyrimidine derivatives. The conformations were overlaid into a three-dimensional cubic box, according to the three-ordered atom alignment. The occupation of the grid cells by the interaction of pharmacophore elements provides the grid cell occupancy descriptors (GCOD), the dependent variables used to build the 4D-QSAR models. The best models were validated (internal and external validation) using several statistical parameters. Docking molecular studies were performed to better understand the binding mode of pyrimidine derivatives inside the TGF-β active site. Results : The 4D-QSAR model presented seven descriptors and acceptable statistical parameters (R2 = 0.89, q2 = 0.68, R2pred = 0.65, r2m = 0.55, R2P = 0.68 and R2rand = 0.21) besides pharmacophores groups important for the activity of these compounds. The molecular docking studies helped to understand the pharmacophoric groups and proposed substituents that increase the potency of aryl pyrimidine derivatives. Conclusion: The best QSAR model showed adequate statistical parameters that ensure their fitness, robustness, and predictivity. Structural modifications were assessed, and five new structures were proposed as candidates for a drug for cancer treatment.

scholarly journals Building an Open Source Institutional Repository at a Small Law School Library: Is it Realistic or Unattainable?

2011 ◽  
Vol 30 (2) ◽  
pp. 81 ◽  
Author(s):  
Fang Wang
Keyword(s):  
Open Source ◽  
School Library ◽  
Law School ◽  
Institutional Repository ◽  
Digital Repository ◽  
Law Faculty ◽  
Development Support ◽  
The Law ◽  
Texas Tech University ◽  
Set Up

Digital preservation activities among law libraries have largely been limited by a lack of funding, staffing and expertise. Most law school libraries that have already implemented an Institutional Repository (IR) chose proprietary platforms because they are easy to set up, customize, and maintain with the technical and development support they provide. The Texas Tech University School of Law Digital Repository is one of the few law school repositories in the nation that is built on the DSpace open source platform.1 The repository is the law school’s first institutional repository in history. It was designed to collect, preserve, share and promote the law school’s digital materials, including research and scholarship of the law faculty and students, institutional history, and law-related resources. In addition, the repository also serves as a dark archive to house internal records.

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