CHEMOGRAPHY CONCEPT IN CHEMICAL SPACE ANALYSIS

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Kernel Target Alignment Parameter: A New Modelability Measure for Regression Tasks

© 2015 American Chemical Society. In this paper, we demonstrate that the kernel target alignment (KTA) parameter can efficiently be used to estimate the relevance of molecular descriptors for QSAR modeling on a given data set, i.e., as a modelability measure. The efficiency of KTA to assess modelability was demonstrated in two series of QSAR modeling studies, either varying different descriptor spaces for one same data set, or comparing various data sets within one same descriptor space. Considered data sets included 25 series of various GPCR binders with ChEMBL-reported pKi values, and a toxicity data set. Employed descriptor spaces covered more than 100 different ISIDA fragment descriptor types, and ChemAxon BCUT terms. Model performances (RMSE) were seen to anticorrelate consistently with the KTA parameter. Two other modelability measures were employed for benchmarking purposes: the Jaccard distance average over the data set (Div), and a measure related to the normalized mean absolute error (MAE) obtained in 1-nearest neighbors calculations on the training set (Sim = 1 - MAE). It has been demonstrated that both Div and Sim perform similarly to KTA. However, a consensus index combining KTA, Div and Sim provides a more robust correlation with RMSE than any of the individual modelability measures

Visualization of a multidimensional descriptor space

© 2016 American Chemical Society.In this chapter, we review some concepts and techniques used to visualize chemical compounds represented as objects in a multidimensional descriptor space. Several modern dimensionality reduction techniques are compared with respect to their ability to visualize the data in 2D space, using as example a dataset of acetylcholinesterase inhibitors and their decoys

Estimation of the size of drug-like chemical space based on GDB-17 data

The goal of this paper is to estimate the number of realistic drug-like molecules which could ever be synthesized. Unlike previous studies based on exhaustive enumeration of molecular graphs or on combinatorial enumeration preselected fragments, we used results of constrained graphs enumeration by Reymond to establish a correlation between the number of generated structures (M) and the number of heavy atoms (N): logM = 0.584 × N × logN + 0.356. The number of atoms limiting drug-like chemical space of molecules which follow Lipinsky's rules (N = 36) has been obtained from the analysis of the PubChem database. This results in M ≈ 1033 which is in between the numbers estimated by Ertl (1023) and by Bohacek (1060). © 2013 Springer Science+Business Media Dordrecht

Bimolecular Nucleophilic Substitution Reactions: Predictive Models for Rate Constants and Molecular Reaction Pairs Analysis

© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Here, we report the data visualization, analysis and modeling for a large set of 4830 SN2 reactions the rate constant of which (logk) was measured at different experimental conditions (solvent, temperature). The reactions were encoded by one single molecular graph – Condensed Graph of Reactions, which allowed us to use conventional chemoinformatics techniques developed for individual molecules. Thus, Matched Reaction Pairs approach was suggested and used for the analyses of substituents effects on the substrates and nucleophiles reactivity. The data were visualized with the help of the Generative Topographic Mapping approach. Consensus Support Vector Regression (SVR) model for the rate constant was prepared. Unbiased estimation of the model's performance was made in cross-validation on reactions measured on unique structural transformations. The model's performance in cross-validation (RMSE=0.61 logk units) and on the external test set (RMSE=0.80) is close to the noise in data. Performances of the local models obtained for selected subsets of reactions proceeding in particular solvents or with particular type of nucleophiles were similar to that of the model built on the entire set. Finally, four different definitions of model's applicability domains for reactions were examined

MOLECULE REPRESENTATION AND DATABASING OF MOLECULES AND REACTIONS

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Predictive cartography of metal binders using generative topographic mapping

© 2017, Springer International Publishing AG. Generative topographic mapping (GTM) approach is used to visualize the chemical space of organic molecules (L) with respect to binding a wide range of 41 different metal cations (M) and also to b uild predictive models for stability constants (logK) of 1:1 (M:L) complexes using “density maps,” “activity landscapes,” and “selectivity landscapes” techniques. A two-dimensional map describing the entire set of 2962 metal binders reveals the selectivity and promiscuity zones with respect to individual metals or groups of metals with similar chemical properties (lanthanides, transition metals, etc). The GTM-based global (for entire set) and local (for selected subsets) models demonstrate a good predictive performance in the cross-validation procedure. It is also shown that the data likelihood could be used as a definition of the applicability domain of GTM-based models. Thus, the GTM approach represents an efficient tool for the predictive cartography of metal binders, which can both visualize their chemical space and predict the affinity profile of metals for new ligands

PREDICTION OF METABOLIC TRANSFORMATIONS OF ORGANIC COMPOUNDS BY CYP1A2 ISOENZYME USING СONDENSED GRAPH OF REACTIONS

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GENERATIVE TOPOGRAPHIC MAPPING OF CONFORMATIONAL SPACE

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