Vsevolod Yu. Tanchuk

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.

Application of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2.1 Program

This article provides a systematic study of several important parameters of the Associative Neural Network (ASNN), such as the number of networks in the ensemble, distance measures, neighbor functions, selection of smoothing parameters, and strategies for the user-training feature of the algorithm. The performance of the different methods is assessed with several training/test sets used to predict lipophilicity of chemical compounds. The Spearman rank-order correlation coefficient and Parzen-window regression methods provide the best performance of the algorithm. If additional user data is available, an improved prediction of lipophilicity of chemicals up to 2-5 times can be calculated when the appropriate smoothing parameters for the neural network are selected. The detected best combinations of parameters and strategies are implemented in the ALOGPS 2.1 program that is publicly available at http://www.vcclab.org/lab/alogps.

Estimation of Aqueous Solubility of Chemical Compounds Using E-State Indices

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.

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.

Internet Software for the Calculation of the Lipophilicity and Aqueous Solubility of Chemical Compounds

In this paper we describe an Internet Java-based technology that allows scientists to make their analytical software available worldwide. The implementation of this technology is exemplified by programs for the calculation of the lipophilicity and water solubility of chemical compounds available at http://www.lnh.unil.ch/~itetko/logp. Both these molecular properties are key parameters in quantitative structure-activity relationship studies and are used to provide invaluable information for the overall understanding of the uptake distribution, biotransformation, and elimination of a wide variety of chemicals. The compounds can be analyzed in batch or single-compound mode. The single-compound analysis offers the possibility to compare our results with several popular lipophilicity calculation methods, including CLOGP, KOWWIN, and XLOGP. The chemical compounds are analyzed according to SMILES line notation that can be prepared with the JME molecular editor of Peter Ertl. Conversion to SMILES from 56 formats is also available using the molecular structure information interchange hub developed by Pat Walters and Matt Stahl.

Inhibition of Yersinia protein tyrosine phosphatase by phosphonate derivatives of calixarenes.

Inhibition of Yersinia protein tyrosine phosphatase by calix[4]arene mono-, bis-, and tetrakis(methylenebisphosphonic) acids as well as calix[4]arene and thiacalix[4]arene tetrakis(methylphosphonic) acids have been investigated. The kinetic studies revealed that some compounds in this class are potent competitive inhibitors of Yersinia PTP with inhibition constants in the low micromolar range. The binding modes of macrocyclic phosphonate derivatives in the enzyme active center have been explained using computational docking approach. The results obtained indicate that calix[4]arenes are promising scaffolds for the development of inhibitors of Yersinia PTP.

Stereoselectivity of binding of α-(N-benzylamino)benzylphosphonic acids to prostatic acid phosphatase

The inhibition effects of enantiomerically pure alpha-(N-benzylamino)benzylphosphonic acids and their derivatives on human prostatic acid phosphatase have been investigated. As expected, (R)-alpha-(N-benzylamino)benzylphosphonic acid demonstrated higher affinity for the enzyme than (S)-enantiomer. At the same time, (1R,2S)-phenyl[(1-phenylethyl)amino]methylphosphonic acid was found to be a significantly weaker inhibitor than its (1S,2R)-analogue. The enantioselectivity has been explained using a molecular modeling approach by computational docking of inhibitors into active center of prostatic acid phosphatase.

Fullerene derivatives as a new class of inhibitors of protein tyrosine phosphatases

In this study, we identified water-soluble C60 and C70 fullerene derivatives as a novel class of protein tyrosine phosphatase inhibitors. The evaluated compounds were found to inhibit CD45, PTP1B, TC-PTP, SHP2, and PTPβ with IC50 values in the low micromolar to high nanomolar range. These results demonstrate a new strategy for designing effective nanoscale protein tyrosine phosphatase inhibitors.

Calix[4]arene methylenebisphosphonic acids as inhibitors of protein tyrosine phosphatase 1B

Сalix[4]arenes bearing methylenebisphosphonic or hydroxymethylenebisphosphonic acid fragments at the wide rim of the macrocycle were studied as inhibitors of PTP1B. Some of the inhibitors showed IC50 values in the micromolar range and good selectivity in comparison with other protein tyrosine phosphatases such as TC-PTP, PTPβ, LAR, and CD45. Kinetic studies indicated that the calix[4]arene derivatives influence PTP1B activity as slow-binding inhibitors. Based on molecular docking results, the binding modes of the macrocyclic bisphosphonates in the active centre of PTP1B are discussed.

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.