Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

Sushko, Iurii; Pandey, Abhed; Novotarskyi, Sergii; Körner, Robert; Rupp, Matthias; Teetz, Wolfram; Brandmaier, Stefan; Abdelaziz, Ahmed; Прокопенко, В. М.; Tanchuk, Vsevolod Yu.; Todeschini, Roberto; Varnek, Alexandre; Marcou, Gilles; Ertl, Peter; Потемкин, В. А.; Grishina, Maria; Gasteiger, Johann; Baskin, Igor I.; Palyulin, Vladimir A.; Radchenko, Eugene V.; Welsh, William J.; Kholodovych, Vladyslav; Chekmarev, Dmitriy S.; Cherkasov, Artem; Aires-de-Sousa, João; Zhang, Qingyou; Bender, Andreas; Nigsch, Florian; Patiny, Luc; Williams, Antony J.; Tkachenko, Valery; Tetko, Igor V.

doi:10.1186/1758-2946-3-s1-p20

Cited by 79 publications

(116 citation statements)

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“…The library was randomly and unbiasedly grouped OCHEM server [10] into the training (120 compounds, Supplementary Figure 1) and test (14 compounds .79168 with RMSE of 0.61459 dGexpt) Gibb's free energy prediction using minimal set of descriptors (Figure 1). A cross-validated correlation coefficient value of 0.70939 (RMSE = 0.72938) was also obtained for the model.…”

Section: Resultsmentioning

confidence: 99%

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Descriptor-based Fitting of Structurally Diverse LPA1 Inhibitors into a Single predictive Mathematical Model

Io¹,

Ueda²

2013

J Phys Chem Biophys

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IntroductionIn situations where protein X-ray structure or related structures for template-based homology modeling are unavailable for structurebased virtual screening, computational methods for drug design rely principally on ligand-based approaches. Ligand-based approach depends on at least one known active compound; which serves as the query for searching library of compounds using predefined molecular descriptor parameters [1,2]. Three categories of chemical descriptors have been characterized till date; physical properties descriptors (1D-descriptor), molecular topology and pharmacophore descriptors (2D-descriptors) and geometrical descriptors (3D-descriptors, often requires prior knowledge of target protein binding-pocket) [3][4][5]. When there are multiple bioactive compounds for a given target, quantitative structure activity relationships (QSARs) method is more beneficial. QSAR method provides predictive mathematical model for biological activities using statistical clustering of multiple descriptors variables [6,7]. We sought to derive a mathematical equation from minimal set of ligand descriptors for set of Lysophosphatidic acid receptor (LPA1) inhibitors. With this equation, we hope to accurately predict the activity of a test set and hopefully used in ligand-based virtual screening for new high-affinity LPA1 antagonists. Materials and MethodsHere, using Molecular Operating Environment (MOE) [8], multiple descriptors (SlogP (SlogP_VSA0-6), SMR (SMR_VSA0-4), a_acc, ASA, E_stb, a_hyd, and Kier (Kier1-2, KierA1-2)) [8] have been generated for training set of compounds (CHEMBL3819) in order to establish a mathematical equation to model LPA1 inhibition (antagonism). PCA analysis was also conducted to determine the principle components of the equation using scientific vector language (SVL) programming built into the MOE. Results and DiscussionFirst, The IC-50 values of 134 unique entries (LPA1 inhibitors) from ChemBL database (CHEMBL3819) were converted to Gibb's free energy of binding using Cheng-Prusoff equation [9] {Equation1} at S<<

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Section: Resultsmentioning

confidence: 99%

“…The library was randomly and unbiasedly grouped OCHEM server [10] into the training (120 compounds, Supplementary Figure 1) and test (14 compounds) sets. The training set was initially fitted using partial least square (PLS) method into all Chemical descriptors implemented in MOE [8].…”

Section: Resultsmentioning

confidence: 99%

Descriptor-based Fitting of Structurally Diverse LPA1 Inhibitors into a Single predictive Mathematical Model

Io¹,

Ueda²

2013

J Phys Chem Biophys

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“…Structures and pK a values of the articles by Tehan et al [6,12] are available at the ''Online Chemical Modeling Environment'' [26] (http://www.ochem.eu, accessed 2012-05-08). Matlab (version 7.6.0, The MathWorks, http://www.mathworks.com) source code for STL and MTL Gaussian process regression can be downloaded from the authors web pages at http://homepages.inf.ed.ac.uk/ gsanguin/software.html, and, http://www.mrupp.info.…”

Section: Data and Descriptorsmentioning

confidence: 99%

Multi-task learning for pKa prediction

Skolidis

Hansen

Sanguinetti

et al. 2012

J Comput Aided Mol Des

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Many compound properties depend directly on the dissociation constants of its acidic and basic groups. Significant effort has been invested in computational models to predict these constants. For linear regression models, compounds are often divided into chemically motivated classes, with a separate model for each class. However, sometimes too few measurements are available for a class to build a reasonable model, e.g., when investigating a new compound series. If data for related classes are available, we show that multi-task learning can be used to improve predictions by utilizing data from these other classes. We investigate performance of linear Gaussian process regression models (single task, pooling, and multitask models) in the low sample size regime, using a published data set (n = 698, mostly monoprotic, in aqueous solution) divided beforehand into 15 classes. A multi-task regression model using the intrinsic model of co-regionalization and incomplete Cholesky decomposition performed best in 85 % of all experiments. The presented approach can be applied to estimate other molecular properties where few measurements are available.

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“…If a problem is identified when checking a small number of structures then the remainder of the dataset can be checked. Another option would be to utilise freely accessible tools developed and supported by public organisations, such as OCHEM (www.ochem.eu) [29]. OCHEM has the functionality to rapidly screen a dataset (chemical name and structure) against PubChem and additionally applies methods to check for structural correctness.…”

Section: Data Used To Build the Model And Consistency Of Model Outputmentioning

confidence: 99%

Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models

Hewitt

Ellison

Cronin

et al. 2015

Advanced Drug Delivery Reviews

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Abstract:The use of in silico tools within the drug development process to predict a wide range of properties including absorption, distribution, metabolism, elimination and toxicity has become increasingly important due to changes in legislation and both ethical and economic drivers to reduce animal testing. Whilst in silico tools have been used for decades there remains reluctance to accept predictions based on these methods particularly in regulatory settings. This apprehension arises in part due to lack of confidence in the reliability, robustness and applicability of the models. To address this issue we propose a scheme for the verification of in silico models that enables end users and modellers to assess the scientific validity of models in accordance with the principles of good computer modelling practice. We report here the implementation of the scheme within the Innovative Medicines Initiative project "eTOX" (electronic toxicity) and its application to the in silico models developed within the frame of this project.

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Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

Cited by 79 publications

References 1 publication

Descriptor-based Fitting of Structurally Diverse LPA1 Inhibitors into a Single predictive Mathematical Model

Descriptor-based Fitting of Structurally Diverse LPA1 Inhibitors into a Single predictive Mathematical Model

Multi-task learning for pKa prediction

Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models

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