Internet technology offers an excellent opportunity for the development of tools by the cooperative effort of various groups and institutions. We have developed a multi-platform software system, Virtual Computational Chemistry Laboratory, http://www.vcclab.org, allowing the computational chemist to perform a comprehensive series of molecular indices/properties calculations and data analysis. The implemented software is based on a three-tier architecture that is one of the standard technologies to provide client-server services on the Internet. The developed software includes several popular programs, including the indices generation program, DRAGON, a 3D structure generator, CORINA, a program to predict lipophilicity and aqueous solubility of chemicals, ALOGPS and others. All these programs are running at the host institutes located in five countries over Europe. In this article we review the main features and statistics of the developed system that can be used as a prototype for academic and industry models.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

The estimation of accuracy and applicability of QSAR and QSPR models for biological and physicochemical properties represents a critical problem. The developed parameter of "distance to model" (DM) is defined as a metric of similarity between the training and test set compounds that have been subjected to QSAR/QSPR modeling. In our previous work, we demonstrated the utility and optimal performance of DM metrics that have been based on the standard deviation within an ensemble of QSAR models. The current study applies such analysis to 30 QSAR models for the Ames mutagenicity data set that were previously reported within the 2009 QSAR challenge. We demonstrate that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs. The presented approach identifies 30-60% of compounds having an accuracy of prediction similar to the interlaboratory accuracy of the Ames test, which is estimated to be 90%. Thus, the in silico predictions can be used to halve the cost of experimental measurements by providing a similar prediction accuracy. The developed model has been made publicly available at http://ochem.eu/models/1 .

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The accuracy of in silico models can be inhomogeneous: models can show excellent performance on some chemical subspaces but have low accuracy on others. We show that applicability domain (AD) approaches can differentiate reliable and non-reliable predictions and identify those with experimental accuracy for both regression and classification models. For reliably predicted molecules, the predicted values can be used instead of experimental measurements. This can halve time and costs of experimental measurements. The developed classification models for AMES mutagenicity test and CYP450 inhibition, which are important drug discovery properties, are publicly available at the online chemical database and modeling environment (OCHEM) site http://qspr.eu

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Background: Due to the increase of multidrug-resistant microorganisms, the search for biologically active molecules does not stop. In the present study, we developed the effective QSAR model which allows a quick search of new potential Staphylococcus aureus inhibitors in the series of quaternary phosphonium salts. A number of the most promising 1,3-oxazol-4-yltriphenylphosphonium derivatives with predicted activities were synthesized and examined to confirm their antibacterial properties and the accuracy of the forecast. Furthermore, the toxicity of the investigated compounds was evaluated. Methods: The predictive QSAR model was developed using Artificial Neural Network approach. Antibacterial properties of the investigated compounds were performed using standard disk diffusion method. The toxicity of the compounds was determined in vivo using zebrafish (Danio rerio) and in vitro on acetylcholinesterase (AChE) enzyme as the test models. Results: The predictive ability of the regression model was tested by cross-validation, giving the cross-validated coefficient q2=0.82. Derivatives of 1,3-oxazol-4-yltriphenylphosphonium salts predicted as active were synthesized and screened for their antibacterial activities. All compounds demonstrated antibacterial activity according to the prediction. The toxicity tests indicated that all investigated samples were less toxic than well-known cationic surfactants. Conclusion: The most promising compound 2b exhibited strong antibacterial activity together with low toxicity and can be considered as a new efficient biocidal agent for future investigation. In addition, the proposed QSAR model can be used for predicting and designing novel potential S. aureus inhibitors among ionic liquids/salts.

The prioritisation of chemical compounds is important for the identification of those chemicals that represent the highest threat to the environment. As part of the CADASTER project (http://www.cadaster.eu), we developed an online web tool that allows the calculation of the environmental risk of chemical compounds from a web interface. The environmental fate of compounds in the aquatic compartment is assessed by using the SimpleBox model, while adverse effects on the aquatic compartment are assessed by the Species Sensitivity Distribution approach. The main purpose of this web tool is to exemplify the use of quantitative structure–activity relationships (QSARs) to support risk assessment. A case study of QSAR integrated risk assessment of 209 polybrominated diphenyl ethers (PBDEs) demonstrates the treatment and influence of uncertainty in the predicted physicochemical and toxicity parameters in probabilistic risk assessment.