Quantum Chemical QSAR Models to Distinguish Between Inhibitory Activities of Sulfonamides Against Human Carbonic Anhydrases I and II and Bovine IV Isozymes

Linear and nonlinear quantitative structure activity relationship models for predicting the inhibitory activities of sulfonamides toward different carbonic anhydrase isozymes were developed based on multilinear regression, principal component-artificial neural network and correlation ranking-principal component analysis, to identify a set of structurally based numerical descriptors. Multilinear regression was used to build linear quantitative structure activity relationship models using 53 compounds with their quantum chemical descriptors. For each type of isozyme, separate quantitative structure activity relationship models were obtained. It was found that the hydration energy plays a significant role in the binding of ligands to the CAI isozyme, whereas the presence of five-membered ring was detected as a major factor for the binding to the CAII isozyme. It was also found that the softness exhibited significant effect on the binding to CAIV isozyme. Principal component-artificial neural network and correlation ranking-principal component analysis analyses provide models with better prediction capability for the three types of the carbonic anhydrase isozyme inhibitory activity than those obtained by multilinear regression analysis. The best models, with improved prediction capability, were obtained for the hCAII isozyme activity. Models predictivity was evaluated by cross-validation, using an external test set and chance correlation test

QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

The terms bioaccumulation and bioconcentration refer to the uptake and build-up of chemicals that can occur in living organisms. Experimental measurement of bioconcentration is time-consuming and expensive, and is not feasible for a large number of chemicals of potential regulatory concern. A highly effective tool depending on a quantitative structure-property relationship (QSPR) can be utilized to describe the tendency of chemical concentration organisms represented by, the important ecotoxicological parameter, the logarithm of Bio Concentration Factor (log BCF) with molecular descriptors for a large set of non-ionic organic compounds. QSPR models were developed using multiple linear regression, partial least squares and neural networks analyses. Linear and non-linear QSPR models to predict log BCF of the compounds developed for the relevant descriptors. The results obtained offer good regression models having good prediction ability. The descriptors used in these models depend on the volume, connectivity, molar refractivity, surface tension and the presence of atoms accepting H-bonds

Airport Characterization for the Adaptation of Surface Congestion Management Approaches

Surface congestion management has received increased attention worldwide, largely due to its potential to mitigate operational inefficiencies and environmental impact. Most prior efforts have focused on demonstrations of a proposed congestion management approach at a particular airport, and not on the adaptation of a particular approach to a range of airport operating environments. This paper illustrates the challenges involved with adapting any class of surface congestion management approaches to different airports. Data and case studies from Boston Logan International Airport, New York’s LaGuardia Airport and Philadelphia International Airport are used to illustrate the diversity in operating environments. The paper then proposes techniques for characterizing airport surface operations using site surveys and operational data. Finally, it shows how these characterizations can be used for the adaptation of a given congestion management approach to different airports.This work was supported by the Federal Aviation Administration’s Office of Environment and Energy through MIT Lincoln Laboratory and the Partnership for AiR Transportation Noise and Emissions Reduction (PARTNER)

Topological modeling of antimycobacterial activity of 3-formyl rifamycin SV derivatives

The paper describes topological modeling of antimycobacterial activity of 3-formyl rifamycin SV derivatives using a large series of molecular vis-à-vis topological descriptors. For the set of 53 derivatives of 3-formyl rifamycin SV no one variable model is possible, however, in multiparametric regression excellent model is obtained for modeling the activity. The results are discussed using variety of statistical parameters

Separation & Quantitative Determination of Be(II) & Ce(IV) with N-m-Tolylm-nitrobenzohydroxamic Acid

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Note on PI and Szeged indices

In theoretical chemistry molecular structure descriptors are used for modeling physico-chemical, pharmacological, toxicologic, biological and other properties of chemical compounds. In this paper we study distance-based graph invariants and present some improved and corrected sharp inequalities for PI, vertex PI, Szeged and edge Szeged topological indices, involving the number of vertices and edges, the diameter, the number of triangles and the Zagreb indices. In addition, we give a complete characterization of the extremal graphs.Comment: 10 pages, 3 figure

A Method of Computing the PI Index of Benzenoid Hydrocarbons Using Orthogonal Cuts

The Padmakar-Ivan (PI) index of a graph G is defined as PI (G) = Σ [neu(e|G)+nev(e|G)], where for edge e=(u,v) are neu (e|G) the number of edges of G lying closer to u than v, and nev (e|G) is the number of edges of G lying closer to v than u and summation goes over all edges of G. The PI index is a Wiener-Szeged-like topological index developed very recently. In this paper we describe a method of computing PI index of benzenoid hydrocarbons (H) using orthogonal cuts. The method requires the finding of number of edges in the orthogonal cuts in a benzenoid system (H) and the edge number of H - a task significantly simpler than the calculation of PI index directly from its definition

Designed Metal-ATCUN Derivatives: Redox- and Non-redox-Based Applications Relevant for Chemistry, Biology, and Medicine

UID/QUI/50006/2019The designed "ATCUN'' motif (amino-terminal copper and nickel binding site) is a replica of naturally occurring ATCUN site found in many proteins/peptides, and an attractive platform for multiple applications, which include nucleases, proteases, spectroscopic probes, imaging, and small molecule activation. ATCUN motifs are engineered at periphery by conjugation to recombinant proteins, peptides, fluorophores, or recognition domains through chemically or genetically, fulfilling the needs of various biological relevance and a wide range of practical usages. This chemistry has witnessed significant growth over the last few decades and several interesting ATCUN derivatives have been described. The redox role of the ATCUN moieties is also an important aspect to be considered. The redox potential of designed M-ATCUN derivatives is modulated by judicious choice of amino acid (including stereochemistry, charge, and position) that ultimately leads to the catalytic efficiency. In this context, a wide range of M-ATCUN derivatives have been designed purposefully for various redox- and non-redox-based applications, including spectroscopic probes, target-based catalytic metallodrugs, inhibition of amyloid-beta toxicity, and telomere shortening, enzyme inactivation, biomolecules stitching or modification, next-generation antibiotic, and small molecule activation.publishersversionpublishe