Descriptor Type Research Articles

Study of chromatographic retention of natural terpenoids by chemoinformatic tools.

The study of chromatographic retention of natural products can be used to increase their identification speed in complex biological matrices. In this work, six variables were used to study the retention behavior in reversed phase liquid chromatography of 39 sesquiterpene lactones (SL) from an in-house database using chemoinformatics tools. To evaluate the retention of the SL, retention parameters on an ODS C-18 column in two different solvent systems were experimentally obtained, namely, MeOH-H2O 55:45 and MeCN-H2O 35:75. The chemoinformatics approach involved three descriptor type sets (one 2D and two 3D) comprising three groups of each (four, five, and six descriptors), two different training and test sets, four algorithms for variable selection (best first, linear forward, greedy stepwise, and genetic algorithm), and two modeling methods (partial least-squares regression and back-propagation artificial neural network). The influence of the six variables used in this study was assessed in a holistic context, and influences on the best model for each solvent system were analyzed. The best set for MeOH-H2O showed acceptable correlation statistics with training R(2) = 0.91, cross-validation Q(2) = 0.88, and external validation P(2) = 0.80, and the best MeCN-H2O model showed much higher correlation statistics with training R(2) = 0.96, cross-validation Q(2) = 0.92, and external validation P(2) = 0.91. Consensus models were built for each chromatographic system, and although all of them showed an improved statistical performance, only one for the MeCN-H2O system was able to separate isomers as well as to improve the performance. The approach described herein can therefore be used to generate reproducible and robust models for QSRR studies of natural products as well as an aid for dereplication of complex biological matrices using plant metabolomics-based techniques.

A descriptor system approach to the delay-dependent exponential stability analysis for switched neutral systems with nonlinear perturbations

In this paper, the problem of exponential stability analysis for switched neutral systems with mixed time-delays and nonlinear perturbations based on descriptor system approach is addressed. Delay-dependent exponential stability results are derived in terms of linear matrix inequalities based on piecewise descriptor type Lyapunov–Krasovskii functional. Average dwell time approach is used to analyze switched neutral systems. In addition convex combination, delay decomposition and free-weighting matrix approaches have been employed to derive less conservative results. Further, numerical examples are illustrated to demonstrate the effectiveness and less conservativeness of the derived theoretical results.

Morphometric approach to pulp fibroblast development in tooth germ.

This paper builds a morphometric framework for the analysis of dental pulp fibroblast evolution during tooth development. We investigated 15 tooth germs (cases) organized, by histological criteria, in three groups corresponding to cap, early bell, and late bell stages, respectively. Each group comprised five cases. The morphometric description used the following parameters: area (A), perimeter (P)—automatically extracted by a color segmentation technique, and form factor (FF)—calculated as 4πA/P 2. The designed framework operated at inter- and intragroup levels. The intergroup analysis quantified the differences between groups, in the sense of a relative distance (RD) adequately defined by mean-value scaling. We showed that the stage of early bell is approximately 5 times closer to late bell than to cap. The quantification procedure required concomitant information about A, P parameters (as P versus A dependences, or FF values), whereas the procedure failed for A or P separately used. The intragroup analysis quantified the similarity of the cases belonging to the same stage. We proved that, unlike the intergroup tests, the individual exploitation of all three descriptors A, P, and FF is effective, yielding highly compatible results. Within any group, most cases presented RDs less than 10% from the group mean value, regardless of the descriptor type.

How Diverse Are Diversity Assessment Methods? A Comparative Analysis and Benchmarking of Molecular Descriptor Space

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.

On a New Model Reduction Method for CFD Systems Used for Flow Control Design

The use of active control to get better characteristics of unsteady internal and external flows is the ultimate goal of the research presented in this paper. Usually, unsteady flows are calculated using Euler and/or Navier-Stokes solvers. The efficiency of numerical simulation of an unsteady flow dramatically increases if the unsteady solution is a small perturbation about a steady-state flow, due to disturbances occurring at the boundaries of the flow domain. The main difficulty related to the flow simulation is that any CFD (Computational Fluid Dynamics) technique generates discrete systems with a very large number of states. In order to design an efficient control, the flow solver must be not only accurate and numerically effective, but also it must have a low number of states. The aim of this paper is to present a new method for model reduction of CFD systems using representative governing equations. The focus is on descriptor type systems resulting from the spatial discretization of the CFD governing equations.

Linear Backward Stochastic Differential Equations of Descriptor Type: Regular Systems

In this article, a class of linear backward stochastic differential equations of descriptor type with time-invariant coefficients are introduced. Necessary and sufficient conditions for their solvability are obtained. It turns out that such equations may not always have a solution, and even when they do, some components of the solution could have a jump at terminal time. Exact controllability of linear descriptor stochastic control systems is also considered.

Guaranteed Cost Control of Multiple-Time-Delay T-S Fuzzy Descriptor Systems

In this paper, we focus on the problem of guaranteed cost control for a class of nonlinear systems described by the Takagi-Sugeno (T-S ) fuzzy descriptor model with multiple time delays. A quadratic cost function is used as a guaranteed performance index. The descriptor type of Lyapunov-Krasovskii functional is employed to analyze the asymptotic stability and design the guaranteed cost controller. The sufficient conditions for the existence of the guaranteed cost controller are given in terms of LMIs. At last, an illustrative example is provided to show the effectiveness of the proposed method.

Robust delay-dependent guaranteed cost controller design for uncertain neutral systems

In this paper, a novel robust delay-dependent guaranteed cost controller is introduced for a class of uncertain nonlinear neutral systems with both norm-bounded uncertainties and nonlinear parameter perturbations. A neutral memory state-feedback control law is chosen such that a quadratic cost function is minimized. On the basis of a descriptor type model transformation, an augmented descriptor form Lyapunov–Krasovskii functional is proposed in order to derive a linear matrix inequality (LMI) condition of the synthesis of a robust stabilizing guaranteed cost controller for uncertain nonlinear neutral systems. The descriptor approach is applied for both the delay-differential equation and the neutral difference operator allowing to introduce several additional free weighting matrices in order to obtain further relaxation in the synthesis process. The proposed method of introducing some relaxation into design problem also leads to develop LMI conditions which can be easily solved using interior point algorithms. Two numerical examples have been introduced to show the application of the theoretical results which indicate that the proposed approach improves the guaranteed cost performance.

Similarity Based Descriptors – Useful for Classification of Substrates of the Human Multidrug Transporter P‐Glycoprotein?

AbstractAs part of the ATP‐binding cassette transporter superfamily P‐glycoprotein (ABCB1) acts as xenotoxic exporter and consequently is strongly involved in multidrug resistance (MDR) and drug‐drug interactions. In this work we focus on our in‐house developed SIBAR approach for prediction of ABCB1 substrates. SIBAR values were calculated on basis of three different descriptor sets: 2D‐MOE descriptors, VSA descriptors and 3D Autocorrelation vectors using in total four reference sets. In order to compare linear with non‐linear classification methods we used binary QSAR and a support vector machine (SVM), respectively. Results demonstrate that with 2D‐MOE and VSA‐descriptors prediction of non substrates performs better, whereas autocorrelation vectors show higher accuracy for substrates. With respect to the different reference sets used in this case selection on basis of maximum diversity yielded better results than a set derived from the training set compounds. In general, the models show distinct differences in their performance depending on the combination of method and descriptor type.

DESIGN OF ROBUST DELAY-DEPENDENT GUARANTEED COST CONTROLLER FOR UNCERTAIN NONLINEAR NEUTRAL SYSTEMS: AN LMI DESCRIPTOR APPROACH

Abstract In this paper, a novel robust delay-dependent guaranteed cost controller is introduced for a class of uncertain nonlinear neutral systems with both norm-bounded uncertainties and nonlinear parameter perturbations. A neutral memory state-feedback control law is chosen such that a quadratic cost function is minimized. On the basis of a descriptor type model transformation, an augmented descriptor form Lyapunov-Krasovskii functional is proposed. A linear matrix inequality (LMI) of synthesis condition is derived. Two numerical examples have been introduced to show the application of the theoretical results.

SwiFT: An Index Structure for Reduced Graph Descriptors in Virtual Screening and Clustering

A reduced graph descriptor represents molecules by small node-labeled graphs. They allow fast similarity calculation, while retaining the overall arrangement of functional groups. The feature tree as an example of this descriptor type abstracts a molecule by a node-labeled, unrooted tree. One available algorithm for pairwise feature tree comparison is the match-search algorithm, which matches the subtrees of two feature trees on each other and therefore creates an alignment. In this work, we document the extension to reuse partial results on the global level of the whole feature tree data set where a high number of identical subtrees exists. The method is based on indexing all occurring subtrees in a data set. On the basis of this index, the similarity value between every subtree combination has to be computed only once. While calculating identical similarities, this approach leads to a substantial reduction in run time by up to 80% and can be used in a parallel computation environment. The search tree built for indexing can also be used to identify duplicated feature trees.

QSAR − How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets

The quality of QSAR (Quantitative Structure-Activity Relationships) predictions depends on a large number of factors including the descriptor set, the statistical method, and the data sets used. Here we study the quality of QSAR predictions mainly as a function of the data set and descriptor type using partial least squares as the statistical modeling method. The study makes use of the fact that we have access to a large number of data sets and to a variety of different QSAR descriptors. The main conclusions are that the quality of the predictions depends both on the data set and the descriptor used. The quality of the predictions correlates positively with the size of the data set and the range of biological activities. There is no clear dependence of the quality of the predictions on the complexity of the data set. All of the descriptors tested produced useful predictions for some of the data sets. None of the descriptors is best for all data sets; it is therefore necessary to test in each individual case, which descriptor produces the best model. In our tests, 2D fragment based descriptors usually performed better than simpler descriptors based on augmented atom types. Possible reasons for these observations are discussed.

Combinatorial QSAR Modeling of P-Glycoprotein Substrates

Quantitative structure-activity (property) relationship (QSAR/QSPR) models are typically generated with a single modeling technique using one type of molecular descriptors. Recently, we have begun to explore a combinatorial QSAR approach which employs various combinations of optimization methods and descriptor types and includes rigorous and consistent model validation (Kovatcheva, A.; Golbraikh, A.; Oloff, S.; Xiao, Y.; Zheng, W.; Wolschann, P.; Buchbauer, G.; Tropsha, A. Combinatorial QSAR of Ambergris Fragrance Compounds. J. Chem. Inf. Comput. Sci. 2004, 44, 582-95). Herein, we have applied this approach to a data set of 195 diverse substrates and nonsubstrates of P-glycoprotein (P-gp) that plays a crucial role in drug resistance. Modeling methods included k-nearest neighbors classification, decision tree, binary QSAR, and support vector machines (SVM). Descriptor sets included molecular connectivity indices, atom pair (AP) descriptors, VolSurf descriptors, and molecular operation environment descriptors. Each descriptor type was used with every QSAR modeling technique; so, in total, 16 combinations of techniques and descriptor types have been considered. Although all combinations resulted in models with a high correct classification rate for the training set (CCR(train)), not all of them had high classification accuracy for the test set (CCR(test)). Thus, predictive models have been generated only for some combinations of the methods and descriptor types, and the best models were obtained using SVM classification with either AP or VolSurf descriptors; they were characterized by CCR(train) = 0.94 and 0.88 and CCR(test) = 0.81 and 0.81, respectively. The combinatorial QSAR approach identified models with higher predictive accuracy than those reported previously for the same data set. We suggest that, in the absence of any universally applicable "one-for-all" QSAR methodology, the combinatorial QSAR approach should become the standard practice in QSPR/QSAR modeling.

Ligand-Based Prediction of Active Conformation by 3D-QSAR Flexibility Descriptors and Their Application in 3+3D-QSAR Models

A conceptionally new 3D molecular descriptor type and methodology are deduced by simple statistical thermodynamic reasoning, based on the free energy change encountered during a transformation of a conformational ensemble of the ligand to an active conformation. The performance of the descriptor was first tested on 37 endomorphin analogues with mu-opiate activity. The method resulted in predictive 3D-QSAR models, and the active conformation was also predicted. Generally, the methodology can be combined with the traditional 3D-QSAR techniques in a 3+3D-QSAR manner. This feature was tested on a series of 38 PGF2alphaprostaglandin analogues with antinidatory activity; the extent to which the molecular flexibility explains the variation in the biological activity was estimated and the active conformation was predicted. The novel descriptors in combination with the grid-based SOMFA descriptors resulted in 3+3D-QSAR models with good levels of predictivity leading to the approach of separation of the effect of the molecular interaction field of the active conformation and the effect of the conformational free energy loss.

Stability and guaranteed cost control of uncertain discrete delay systems

Robust stability and the guaranteed cost control problem are considered for discrete-time systems with time-varying delays from given intervals. A new construction of Lyapunov–Krasovskii functionals (LKFs), which has been recently introduced in the continuous-time case, is applied. To a nominal LKF, which is appropriate to the system with nominal delays, terms are added that correspond to the system with the perturbed delays and that vanish when the delay perturbations approach zero. The nominal LKF is chosen in the form of the descriptor type and is applied either to the original or to the augmented system. The delay-independent result is derived via the Razumikhin approach. Guaranteed cost state-feedback control is designed. The advantage of the new tests is demonstrated via illustrative examples.

ROBUST STABILITY OF UNCERTAIN DISCRETE SYSTEMS WITH TIME-VARYING DELAY

Robust stability is considered for uncertain discrete-time systems with time-varying delays from given intervals. A new construction of Lyapunov-Krasovskii functions (LKFs), which has been recently introduced in the continuous case, is applied: to a nominal LKF, which is appropriate to the system with nominal delays, terms are added that correspond to the system with the perturbed delays and that vanish when the delay perturbations approach 0. The nominal LKF is chosen in two forms: the descriptor type and the ‘exact’ one. The delay-independent result is derived via Razumikhin approach. The advantage of the new tests is demonstrated via illustrative examples.

An automated PLS search for biologically relevant QSAR descriptors

An automated PLS engine, WB-PLS, was applied to 1632 QSAR series with at least 25 compounds per series extracted from WOMBAT (WOrld of Molecular BioAcTivity). WB-PLS extracts a single Y variable per series, as well as pre-computed X variables from a table. The table contained 2D descriptors, the drug-like MDL 320 keys as implemented in the Mesa A&C Fingerprint module, and in-house generated topological-pharmacophore SMARTS counts and fingerprints. Each descriptor type was treated as a block, with or without scaling. Cross-validation, variable importance on projections (VIP) above 0.8 and q2 > or = 0.3 were applied for model significance. Among cross-validation methods, leave-one-in-seven-out (CV7) is a better measure of model significance, compared to leave-one-out (measuring redundancy) and leave-half-out (too restrictive). SMARTS counts overlap with 2D descriptors (having a more quantitative nature), whereas MDL keys overlap with in-house fingerprints (both are more qualitative). The SMARTS counts is the most effective descriptor system, when compared to the other three. At the individual level, size-related descriptors and topological indices (in the 2D property space), and branched SMARTS, aromatic and ring atom types and halogens are found to be most relevant according to the VIP criterion.