Volodymyr V. Prokopenko

Virtual computational chemistry laboratory - design and description

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.

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

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.

Predictive QSAR modeling of phosphodiesterase 4 inhibitors

A series of diverse organic compounds, phosphodiesterase type 4 (PDE-4) inhibitors, have been modeled using a QSAR-based approach. 48 QSAR models were compared by following the same procedure with different combinations of descriptors and machine learning methods. QSAR methodologies used random forests and associative neural networks. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q² = 0.66-0.78 for regression models and total accuracies Ac=0.85-0.91 for classification models. Predictions for the external evaluation sets obtained accuracies in the range of 0.82-0.88 (for active/inactive classifications) and Q² = 0.62-0.76 for regressions. The method showed itself to be a potential tool for estimation of IC₅₀ of new drug-like candidates at early stages of drug development.

Design and Synthesis of New Potent Inhibitors of Farnesyl Pyrophosphate Synthase

Predictive QSAR models for the inhibition activities of nitrogen-containing bisphosphonates (N-BPs) against farnesyl pyrophosphate synthase (FPPS) from Leishmania major (LeFPPS) were developed using a data set of 97 compounds. The QSAR models were developed through the use of Artificial Neural Networks and Random Forest learning procedures. The predictive ability of the models was tested by means of leave-one-out cross-validation; Q(2)values ranging from 0.45-0.79 were obtained for the regression models. The consensus prediction for the external evaluation set afforded high predictive power (Q(2)=0.76 for 35 compounds). The robustness of the QSAR models was also evaluated using a Y-randomization procedure. A small set of 6 new N-BPs were designed and synthesized applying the Michael reaction of tetrakis (trimethylsilyl) ethenylidene bisphosphonate with amines. The inhibition activities of these compounds against LeFPPS were predicted by the developed QSAR models and were found to correlate with their fungistatic activities against Candida albicans. The antifungal activities of N-BPs bearing n-butyl and cyclopropyl side chains exceeded the activities of Fluconazole, a triazole-containing antifungal drug. In conclusion, the N-BPs developed here present promising candidate drugs for the treatment of fungal diseases.

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

The Online Chemical Modeling Environment is a unique platform on the Web 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. The database is user-contributed and 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 on data quality and verification. 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. Our intention is to make OCHEM an ultimate platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The OCHEM is free for the web users and it is available online at http://ochem.eu. “Computing chemistry on the web” [1] is becoming a reality.

A web portal for classification of expression data using maximal margin linear programming

The Maximal Margin (MAMA) linear programming classification algorithm has recently been proposed and tested for cancer classification based on expression data. It demonstrated sound performance on publicly available expression datasets. We developed a web interface to allow potential users easy access to the MAMA classification tool. Basic and advanced options provide flexibility in exploitation. The input data format is the same as that used in most publicly available datasets. This makes the web resource particularly convenient for non-expert machine learning users working in the field of expression data analysis.

OCHEM - on-line CHEmical database & modeling environment

The main goal of OCHEM database http://qspr.eu is to collect, store and manipulate chemical data with the purpose of their use for model development. Its main features, that distinguish it from other available databases include: 1. The database is open and it is based on Wiki-style principles. We encourage users to submit data and to correct inaccurate submitted data; 2. The database is aimed at collecting high-quality data. To achieve this we require users to submit references to the article, where the data was published. The reference may include the article name, journal name, date of publication, page number, line number, etc. 3. Since the compound properties may vary depending on the conditions, under which they were measured, we store the measurement conditions with the data to provide the users with more accurate information about each data point. The modeling framework is being developed to complement the Wiki-style database of chemical structures. Its main goal is to provide a flexible and expandable calculation environment that would allow a user to create and manipulate QSAR and QSPR models on-line. The modeling framework is integrated with the database web-interface that allows easy transfer of database data to the models. The web interface of the modeling environment is aimed to provide to the Web users easy means to create high-quality prediction models and estimate their accuracy of prediction and applicability domain. The developed models can be published on the Web and be accessed by other users to predict new molecules on-line. This tool is aimed to generate a new paradigm for structure activity relationship knowledge bases, making QSAR/QSPR models active, user-contributed and easily accessible for benchmarking, general use and educational purposes. The examples of the use of the database within national and EU projects will be exemplified.

Prediction of Thrombin and Factor Xa Inhibitory Activity with Associative Neural Networks

Quantitative structure-activity relationship studies on a series of selective inhibitors of thrombin and factor Xa were performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was performed. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2=0.74-0.87 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.71-0.82 for regressions. The proposed models can be potential tools for finding new drug candidates.