Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations

Russian Chemical Bulletin - We describe the current version of the PASS program for prediction of biological activity spectra of organic compounds based on analysis of structure—activity...     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

Synthesis, characterization, and prediction of biological activity of phosphoramide compounds

In this study, the synthesis and characterization of some phosphoramides are reported. In addition, their insecticidal activity and probable toxicity were predicted using the PASS software. Partial multiple bond formation and charge distribution may be responsible for the anti-AChE activity in phosphoramides.     

Computational platform Way2Drug: from the prediction of biological activity to drug repurposing

The Way2Drug informational-computational platform (www.way2drug.com/dr) provides access to the data on drugs approved for medicinal use in the USA and Russian Federation, as well as computational possibilities for the prediction of biological activity of drug-like organic compounds. Currently realized computational tools of the platform, which allow one to predict several thousands of biological activity types, including the interaction with molecular targets, pharmacotherapeutic and side effects, metabolism, acute toxicity for rats, cytotoxicity, influence on gene expression, and other properties characterizing the evaluation how promising are particular drug-like compounds as potential pharmaceuticals, are reviewed. Using the Way2Drug platform, one can not only select the most promising "hits" for the synthesis and testing of biological activity but also reveal new indications for the launched drugs.     

Chemometrical analysis of computed QSAR parameters and their use in biological activity prediction

This study gives a quantitative structure-activity relationship (QSAR) correlation of the 72 N-benzylsalicylamide derivatives properties with their antimycobacterial activity. The antimycobacterial activity was measured as the minimal inhibition concentration (MIC) determined for four strains of mycobacterium (M. avium, M. kansasii, M. kansasii clin.-clinically isolated form, and M. tuberculosis) after 14 days and after 21 days of cultivation. The objective was to identify the factors most closely defining biological activity of N-benzylsalicylamides, in order to enable QSAR prediction of new derivatives with high antimycobacterial activity. Optimal properties for the QSAR analysis were selected from several physicochemical properties, including lipophilicity parameter log P, molecular mass M, molar refraction MR, NMR chemical shifts, polarizability, etc. Many of the considered properties are different from those typically used in traditional QSAR. Selection of the most important properties was performed by one-way Analysis of Variance (ANOVA) and correlation analysis using the significance coefficients and the correlation coefficients, respectively. The chosen variables were further used in artificial neural networks (ANN) for predicting biological activity in the form of-log(MIC). Presented at the 1st International Conference “Applied Natural Sciences” on the occasion of the 10th anniversary of the University of Ss. Cyril and Methodius, Trnava, 7–9 November 2007.     

Computer aided prediction of biological activity spectra: Evaluating versus known and predicting of new activities for thiazole derivatives

Computer aided prediction of biological activity spectra by the computer program PASS was applied to a set of 89 new thiazole derivatives. Experimentally tested activities (NSAID, local anaesthetic and antioxidant) coincide with the experiment in 70.8% cases, that exceeds significantly the random guess-work (～0.1%). Therefore, computer aided prediction using the Prediction of Activity Spectra for Substances (PASS) system (http://www.ibmh.msk.su/PASS) provides a reliable basis for planning of synthesis and experimental study for new compounds. New psychotropic activities are predicted for some compounds from the series under study. In particular, 7, 44 and 55 compounds likely have anxiolytic, anticonvulsant and cognition enhancer effects, respectively. Most of these compounds have the estimated values of probability to be active ( P a ) less than 60%. Therefore, if their activity will be confirmed by the experiment, they might occur to be New Chemical Entities.     

Computer aided prediction of biological activity spectra: evaluating versus known and predicting of new activities for thiazole derivatives

Computer aided prediction of biological activity spectra by the computer program PASS was applied to a set of 89 new thiazole derivatives. Experimentally tested activities (NSAID, local anaesthetic and antioxidant) coincide with the experiment in 70.8% cases, that exceeds significantly the random guess-work (approximately 0.1%). Therefore, computer aided prediction using the Prediction of Activity Spectra for Substances (PASS) system (http://www.ibmh.msk.su/PASS) provides a reliable basis for planning of synthesis and experimental study for new compounds. New psychotropic activities are predicted for some compounds from the series under study. In particular, 7, 44 and 55 compounds likely have anxiolytic, anticonvulsant and cognition enhancer effects, respectively. Most of these compounds have the estimated values of probability to be active (Pa) less than 60%. Therefore, if their activity will be confirmed by the experiment, they might occur to be New Chemical Entities.     

Optimal descriptor as a translator of eclectic information into the prediction of thermal conductivity of micro-electro-mechanical systems

Micro-electro-mechanical systems (MEMS) are involved in various fields of nanotechnology. MEMS are characterized by complex and unclear molecular architecture. However, in most cases information about chemical composition and conditions of synthesis is available. One-variable models for thermal conductivity of MEMS are suggested. These models are based on the representation of MEMS by their chemical composition and technological attributes. We have examined three random splits of available data into the training set and validation set. The average statistical characteristics of these models are quite good. Development of suggested here models is carried out without information on molecular architecture of MEMS.     

Synthesis and computational prediction of biological activity of 5-(nitrophenyl)-1,4,6-oxadithiocanes

5-(Nitrophenyl)-1,4,6-oxadithiocanes have been obtained by the reaction of p- and m-nitrobenzaldehydes with 2,2’-oxydiethanethiol in the presence of trimethylchlorosilane. The structure of the products has been proved by IR, NMR, and mass spectra.