CORAL Software: Analysis of Impacts of Pharmaceutical Agents Upon Metabolism via the Optimal Descriptors

QSPR/QSAR: State-of-Art, Weirdness, the Future

Ability of quantitative structure-property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the "statistical similarity" of different endpoints is suggested and illustrated by an example for relatively "distanced each from other" endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood-brain barrier.

Whether the Validation of the Predictive Potential of Toxicity Models is a Solved Task?

Different kinds of biological activities are defined by complex biochemical interactions, which are termed as a "mathematical function" not only of the molecular structure but also for some additional circumstances, such as physicochemical conditions, interactions via energy and information effects between a substance and organisms, organs, cells. These circumstances lead to the great complexity of prediction for biochemical endpoints, since all "details" of corresponding phenomena are practically unavailable for the accurate registration and analysis. Researchers have not a possibility to carry out and analyse all possible ways of the biochemical interactions, which define toxicological or therapeutically attractive effects via direct experiment. Consequently, a compromise, i.e. the development of predictive models of the above phenomena, becomes necessary. However, the estimation of the predictive potential of these models remains a task that is solved only partially. This mini-review presents a collection of attempts to be used for the above-mentioned task, two special statistical indices are proposed, which may be a measure of the predictive potential of models. These indices are (i) Index of Ideality of Correlation; and (ii) Correlation Contradiction Index.

Virtual Screening of Anti-Cancer Compounds: Application of Monte Carlo Technique

Possibility and necessity of standardization of predictive models for anti-cancer activity are discussed. The hypothesis about rationality of common quantitative analysis of anti-cancer activity and carcinogenicity is developed. Potential of optimal descriptors to be used as a tool to build up predictive models for anti-cancer activity is examined from practical point of view. Various perspectives of application of optimal descriptors are reviewed. Stochastic nature of phenomena which are related to carcinogenic potential of various substances can be successfully detected and interpreted by the Monte Carlo technique. Hypothesises related to practical strategy and tactics of the searching for new anticancer agents are suggested.

In silico design of diacylglycerol acyltransferase-1 (DGAT1) inhibitors based on SMILES descriptors using Monte-Carlo method

Diabetes, obesity and other diseases related to metabolism are worldwide health problems. These syndromes can be well treated when a particular enzyme-based therapy is developed. Diacylglycerol acyltransferase (DGAT; EC 2.3.1.20) is a microsomal enzyme which is responsible for the synthesis of triglycerides from 1,2-diacylglycerol by catalyzing the acyl-CoA-dependent acylation. The obesity and type-II diabetes can be checked by the inhibition of DGAT1 enzyme. Quantitative structure-activity relationship (QSAR) modelling is an essential technique in drug design and development. To study the aspect of DGAT1 inhibitors, Monte-Carlo method-based QSAR was developed for 197 DGAT1 inhibitors. QSAR models were derived by using the optimal descriptor based on SMILES notation. Different statistical parameters including the novel index of ideality of correlation were applied to validate the generated QSAR models. Four random splits were prepared from the data set. The statistical criteria r² = 0.8129, CCC = 0.8979 and Q² = 0.7962 of the validation set of split 1 were the best; therefore, the developed QSAR model of split 1 was decided to be the leading model. The molecular fragments, which were promoter of endpoint increase or decrease were also determined. Thirteen new DGAT1 inhibitors were designed from the lead compound DGAT011.

Prediction of Biochemical Endpoints by the CORAL Software: Prejudices, Paradoxes, and Results

Quantitative structure-activity relationships (QSARs) for prediction of toxicological endpoints built up with the CORAL software are discussed. Prejudices related to these QSAR models are listed. Possible ways to improve the software are discussed.