Overall connectivity--a next generation molecular connectivity

Similarity-Based Virtual Screening to Find Antituberculosis Agents Based on Novel Scaffolds: Design, Syntheses and Pharmacological Assays

A method to identify molecular scaffolds potentially active against the Mycobacterium tuberculosis complex (MTBC) is developed. A set of structurally heterogeneous agents against MTBC was used to obtain a mathematical model based on topological descriptors. This model was statistically validated through a Leave-n-Out test. It successfully discriminated between active or inactive compounds over 86% in database sets. It was also useful to select new potential antituberculosis compounds in external databases. The selection of new substituted pyrimidines, pyrimidones and triazolo[1,5-a]pyrimidines was particularly interesting because these structures could provide new scaffolds in this field. The seven selected candidates were synthesized and six of them showed activity in vitro.

A simple model to solve a complex drug toxicity problem

Linear drug toxicity models like therapeutic index (TI), physicochemical rules (rule of five, 3/75), ligand efficiency indices (LEI), ideal pharmacokinetic (PK) and pharmacodynamic (PD) profiles are widely used in drug discovery and development. In spite of this, predicting drug toxicity at various stages remains challenging and the overall productivity (<20%) and ultimate benefit to the patients remain low. A simple drug toxicity model, "Drug Toxicity Index" (DTI), is developed here using 711 oral drugs. DTI redefines drug toxicity as scaled biphasic and exponential functions of PD, PK and physicochemical parameters. PD, PK and physicochemical toxicity contributions were estimated from the on and off target IC₅₀, maximum unbound plasma drug concentration (free C _max), and log D values, respectively. These contributions are then scaled by molar dose and oral bioavailability and the logarithm of the sum of scaled contributions is DTI. Drugs with DTI above the WHO ATC drug category specific average values consistently have toxic profiles, while drugs with DTI below this average are relatively safe. DTI performs better than standard rules for lead optimization, LEI and exposure based TIs in identifying safe and toxic drugs. DTI classifies 392 drugs reported in the US-FDA's Liver Toxicity Knowledge Base (LTKB) with an AUC for ROC curves of 0.91-0.64 for different WHO ATC categories. DTI has been used to predict network meta-analysis results on relative toxicity within/across eight different therapeutic areas. It is useful in understanding PD, PK and physicochemical toxicity contributions and identifying potentially toxic drugs and the toxicity of recently approved drugs. Decision trees are proposed for applying the DTI concept in preclinical drug discovery and clinical trial settings. DTI can potentially reduce failure in drug discovery and might be useful in therapeutic drug monitoring and in xenobiotic and environmental toxicity studies.

Search of Chemical Scaffolds for Novel Antituberculosis Agents

A method to identify chemical scaffolds potentially active against Mycobacterium tuberculosis is presented. The molecular features of a set of structurally heterogeneous antituberculosis drugs were coded by means of structural invariants. Three techniques were used to obtain equations able to model the antituberculosis activity: linear discriminant analysis, multilinear regression, and shrinkage estimation–ridge regression. The model obtained was statistically validated through leave- n-out test, and an external set and was applied to a database for the search of new active agents. The selected compounds were assayed in vitro, and among those identified as active stand reserpine, N,N,N′,N′-tetrakis-(2-pyridylmethyl)-ethylenediamine (TPEN), trifluoperazine, pentamidine, and 2-methyl-4,6-dinitro-phenol (DNOC). They show activity comparable to or superior to ethambutol, used in combination with other drugs for the prevention and treatment of Mycobacterium avium complex and drug-resistant tuberculosis.

Comparative study of topological indices of macro/supramolecular RNA complex networks

RNA function annotation is often based on alignment to a previously studied template. In contrast to the study of proteins, there are not many alignment-free methods to predict RNA functions if alignment fails. The use of topological indices (TIs) of RNA complex networks (CNs) to find quantitative structure-activity relationships (QSAR) may be an alternative to incorporate secondary structure or sequence-to-sequence similarity. Here, we introduce new QSAR-like techniques using RNA macromolecular CNs (mmCNs), where nodes are nucleotides, or RNA supramolecular CNs (smCNs), where nodes are RNA sequences. We studied a data set of 198 sequences including 18S-rRNAs (important phylogenetic molecular biomarkers). We constructed three types of RNA mmCNs: sequence-linear (SL), Cartesian-lattice (CL), and sequence-folding CNs (SF-CNs) and two smCNs: sequence-sequence disagreement CN (SSD) and sequence-sequence similarity (SSS-smCN). We reported the first comparative QSAR study with all these CIs and CNs, which includes: (i) spectral moments ( ( i )micro d ( w)) of SL-mmCNs (accuracy = 75.3%), (ii) electrostatic CIs (xi d ) of CL-mmCNs (>90%), (iii) thermodynamic parameters (Delta G, Delta H, Delta S, and T m) of SF-mmCNs (64.7%), (iv) disagreement-distribution moments ( M k ) of the SSD-smCN (79.3%), and (v) node centralities of the SSD-smCN (78.0%). Furthermore, we reported the experimental isolation of a new RNA sequence from Psidum guajava leaf tissue and its QSAR and BLAST prediction to illustrate the practical use of these methods. We also investigated the use of these CNs to explore rRNA diversity on bacteria, plants, and parasites from the Dactylogyrus genus. The HPL-mmCNs model was the best of all found. All the CNs and TIs, except SF-mmCNs, were introduced here by the first time for the QSAR study of RNA, which allowed a comparative study for RNA classification.

QSPR Using MOLGEN-QSPR: The Challenge of Fluoroalkane Boiling Points

By means of the new software MOLGEN-QSPR, a multilinear regression model for the boiling points of lower fluoroalkanes is established. The model is based exclusively on simple descriptors derived directly from molecular structure and nevertheless describes a broader set of data more precisely than previous attempts that used either more demanding (quantum chemical) descriptors or more demanding (nonlinear) statistical methods such as neural networks. The model's internal consistency was confirmed by leave-one-out cross-validation. The model was used to predict all unknown boiling points of fluorobutanes, and the quality of predictions was estimated by means of comparison with boiling point predictions for fluoropentanes.

QSPR Using MOLGEN-QSPR: The Example of Haloalkane Boiling Points

MOLGEN-QSPR is a software newly developed for use in quantitative structure property relationships (QSPR) work. It allows to import, to manually edit, or to generate chemical structures, to detect duplicate structures, to import or to manually input property values, to calculate the values of a broad pool of molecular descriptors, to establish QSPR equations (models), and using such models to predict unknown property values. In connection with the molecule generator MOLGEN, MOLGEN-QSPR is able to predict property values for all compounds in a predetermined structure space (inverse QSPR). Some of the features of MOLGEN-QSPR are demonstrated on the example of haloalkane boiling points. The data basis used here is broader than in previous studies, and the models established are both more precise and simpler than those previously reported.

Combinatorial QSAR of Ambergris Fragrance Compounds

A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

Modified Connectivity Indices and Their Application to QSPR Study

A modified adjacency matrix was developed to delineate the chemical graph of a compound, in which the element a(ii) along the diagonal of the matrix reflects the numbers of the lone-pair electrons and pi bonds of the ith atom, and the off-diagonal element a(ij) of the matrix characterizes whether the jth non-hydrogen atom is bonded to the ith non-hydrogen atom as well as the number of hydrogen atoms bonded to the jth non-hydrogen atom. The corresponding vertex-degree matrix can distinguish the non-hydrogen atoms in the compound better than that from the original adjacency matrix. Based on the newly proposed adjacency matrix, modified molecular connectivity indices (mMCIs) were proposed as structural descriptors for organic compounds, which were applied to the QSPR studies on the boiling point temperature, molar volume and molar refraction of alkanes, alkenes and alcohols. The results show that, in most cases, the mMCIs give improved correlations than the original molecular connectivity indices (MCIs), which are particularly suitable to distinguish isomers.

On the complexity of directed biological networks

Recently there was an increased interest towards network approach to biology and environmental sciences. Networks are believed to be the key to the understanding of the work of biological machine in cells, organs, organisms, and ecosystems. While complexity of undirected networks has been recently analyzed, the assessment of complexity in directed networks has specificity that has not been explored so far. The present paper aims to address the existing gap by discussing the applicability of the available complexity descriptors. New specific measures (vertex accessibility, accessible connectedness, and adjusted average distance) are introduced based on assessment of the reduced accessibility of nodes in directed networks.

QSAR modeling using chirality descriptors derived from molecular topology

Topological descriptors of chemical structures (such as molecular connectivity indices) are widely used in Quantitative Structure-Activity Relationships (QSAR) studies. Unfortunately, these descriptors lack the ability to discriminate between stereoisomers, which limits their application in QSAR. To circumvent this problem, we recently introduced chirality descriptors derived from molecular graphs and applied them in QSAR studies of ecdysteroids (Golbraikh A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001,41, 147-158). In this paper, we extend our earlier work by applying chirality descriptors to four data sets containing chiral compounds. All models were derived with the k-nearest neighbors (kNN) QSAR method developed in our laboratory (Zheng, W.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2000, 40, 185-194). They were validated using the same training and test sets that were employed in various, mostly 3D-QSAR, investigations published by other authors. We show that for all data sets 2D-QSAR models that use a combination of chirality descriptors with conventional (chirality insensitive) topological descriptors afford better or similar predictive ability as compared to models generated with 3D-QSAR approaches. The results presented in this paper reassure that 2D-QSAR modeling provides a powerful alternative to 3D-QSAR.

Novel ZE-isomerism descriptors derived from molecular topology and their application to QSAR analysis

We introduce several series of novel ZE-isomerism descriptors derived directly from two-dimensional molecular topology. These descriptors make use of a quantity named ZE-isomerism correction, which is added to the vertex degrees of atoms connected by double bonds in Z and E configurations. This approach is similar to the one described previously for topological chirality descriptors (Golbraikh, A., et al. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). The ZE-isomerism descriptors include modified molecular connectivity indices, overall Zagreb indices, extended connectivity, overall connectivity, and topological charge indices. They can be either real or complex numbers. Mathematical properties of different subgroups of ZE-isomerism descriptors are discussed. These descriptors circumvent the inability of conventional topological indices to distinguish between Z and E isomers. The applicability of ZE-isomerism descriptors to QSAR analysis is demonstrated in the studies of a series of 131 anticancer agents inhibiting tubulin polymerization.

Issues in representation of molecular structure the development of molecular connectivity

Significant issues in the representation of molecular structure and the development of the molecular connectivity paradigm are presented. In the molecular connectivity paradigm, molecular structure is represented directly. Kier and Hall developed the method by creating ways to encode electronic information based on the paradigm developed from the Randić branching index. The simple and valence delta values were created to encode atomic and valence-state electronic information through counts of sigma, pi, and lone pair electrons. A family of indices was created to provide a wide range of structure information. The key aspects of the development are presented and discussed in such a way as to reveal, at least in part, the imaginative thinking involved in the process. Possible future roles for molecular connectivity chi indices are discussed.

Substructure, subgraph, and walk counts as measures of the complexity of graphs and molecules

In discussions of unsaturated compounds represented by multigraphs it is necessary to distinguish between the notions of substructure and subgraph. Here the difference is explained and exemplified, and a computer program is introduced which for the first time is able to construct and count all substructures and subgraphs for a colored multigraph (a molecular compound which may contain unsaturation and heteroatoms). Construction of all substructures and subgraphs is computationally demanding; therefore, two alternatives are pointed out for the treatment of large sets of compounds: (i) Often it will suffice to consider counts of substructures/subgraphs up to a certain number of edges only, information which is provided by the program much more rapidly. (ii) It is shown that information equivalent to that gained from substructure or subgraph counts is often far more easily available using walk counts. Some problems and their consequences for substructure/subgraph/walk counts are discussed that arise from the models used in organic chemistry for certain compounds such as aromatics and from the necessity to express qualitative features of molecular structures numerically.