Computing Topological Descriptors and Polynomials of Certain 2D Chemical Structures

In the fields of mathematical chemistry, a topological index, also known as a connectivity index, is a type of a molecular descriptor that is calculated based on the molecular graph of a chemical compound. Topological indices are an analytical framework of a graph which portray its topology and are mostly equal graphs. Topological indices (TIs) are numeral quantities that are used to foresee the natural correlation among the physicochemical properties of the chemical compounds in their fundamental network. TIs show an essential role in the theoretical abstract and environmental chemistry and pharmacology. In this paper, we compute many latest developed degree-based TIs. An analogy among the computed different versions of the TIs with the help of the numerical values and their graphs is also included .In this article, we compute the first Zagreb index, second Zagreb index, hyper Zagreb index, ABC Index, GA Index, and first Zagreb polynomial and second Zagreb polynomial of chemical graphs polythiophene, nylon 6,6, and the backbone structure of DNA.

An Optimized Modeling Algorithm for Breast Cancer Drug Candidates Based on NSGAII

Breast cancer is one of the most common malignant tumors in women. It seriously threatens the safety of women worldwide. It is an important and urgent task to research and develop anti-breast cancer drugs and improve the therapeutic effect of breast cancer. Taking the actual sample data as the main starting point, firstly, the prediction model of pIC50 is established by ResNet residual network and neural network (NN) to judge the biological activity. Then the classification model of ADMET property is established by ResNet residual network and LightGBM, and the model fusion is realized by Choquet fuzzy integral. Finally, the NSGAII multi-objective optimization algorithm is used to determine the range of values that each molecular descriptor obtains in the range of good biological activity, and ultimately to optimize the modeling of anti-breast cancer drug candidates. The experimental results show that the algorithm improves the prediction accuracy of biological activity, realizes the efficient and accurate classification of ADMET properties, and accurately describes the impact of molecular descriptors on biological activity.

QSPR Models for the Molar Refraction, Polarizability and Refractive Index of Aliphatic Carboxylic Acids Using the ZEP Topological Index

The molar refraction, polarizability, and refractive index for a series of monocarboxylic, dicarboxylic, and unsaturated monocarboxylic acids, having a symmetric or asymmetric structure, were investigated by the application of quantitative structure property relationship (QSPR) technique. We used a linear regression method and a single molecular descriptor, the ZEP topological index, calculated in a simple manner, with the help of weighted electronic distances, and also calculated on the basis of the chemical structure of the molecules. The high-quality performance and predictive ability of the QSPR models obtained were validated by means of specific validation techniques: y-randomization test, the leave-one-out cross validation procedure, and external validation. The investigated properties are well modeled (with r2 > 0.99) by the ZEP index, using the regression analysis as a statistical tool for developing reliable QSPR models. Our approach provides an alternative technique to the existing additive methods for predicting the molar refraction and polarizability of carboxylic acids, which is essentially based on the summation of atom and/or functional group contributions or bond contributions, and of some correction increments.

Combined Pharmacophore and Grid-Independent Molecular Descriptors (GRIND) Analysis to Probe 3D Features of Inositol 1,4,5-Trisphosphate Receptor (IP3R) Inhibitors in Cancer

Inositol 1, 4, 5-trisphosphate receptor (IP3R)-mediated Ca2+ signaling plays a pivotal role in different cellular processes, including cell proliferation and cell death. Remodeling Ca2+ signals by targeting the downstream effectors is considered an important hallmark in cancer progression. Despite recent structural analyses, no binding hypothesis for antagonists within the IP3-binding core (IBC) has been proposed yet. Therefore, to elucidate the 3D structural features of IP3R modulators, we used combined pharmacoinformatic approaches, including ligand-based pharmacophore models and grid-independent molecular descriptor (GRIND)-based models. Our pharmacophore model illuminates the existence of two hydrogen-bond acceptors (2.62 Å and 4.79 Å) and two hydrogen-bond donors (5.56 Å and 7.68 Å), respectively, from a hydrophobic group within the chemical scaffold, which may enhance the liability (IC50) of a compound for IP3R inhibition. Moreover, our GRIND model (PLS: Q2 = 0.70 and R2 = 0.72) further strengthens the identified pharmacophore features of IP3R modulators by probing the presence of complementary hydrogen-bond donor and hydrogen-bond acceptor hotspots at a distance of 7.6–8.0 Å and 6.8–7.2 Å, respectively, from a hydrophobic hotspot at the virtual receptor site (VRS). The identified 3D structural features of IP3R modulators were used to screen (virtual screening) 735,735 compounds from the ChemBridge database, 265,242 compounds from the National Cancer Institute (NCI) database, and 885 natural compounds from the ZINC database. After the application of filters, four compounds from ChemBridge, one compound from ZINC, and three compounds from NCI were shortlisted as potential hits (antagonists) against IP3R. The identified hits could further assist in the design and optimization of lead structures for the targeting and remodeling of Ca2+ signals in cancer.

Forgotten Coindex for the Derived Sum Graphs under Cartesian Product

A topological index (TI) is a molecular descriptor that is applied on a chemical structure to compute the associated numerical value which measures volume, density, boiling point, melting point, surface tension, or solubility of this structure. It is an efficient mathematical method in avoiding laboratory experiments and time-consuming. The forgotten coindex of a structure or (molecular) graph H is defined as the sum of the degrees of all the possible pairs of nonadjacent vertices in H . For D ∈ S , R , Q , T and the connected graph H , the derived graphs D H are obtained by applying the operations S (subdivided), R (triangle parallel), Q (line superposition), and T (total graph), respectively. Moreover, a derived sum graph ( D -sum graph) is obtained by the Cartesian product of the graph H 2 with the graph D H 1 . In this study, we compute forgotten coindex of the D -sum graphs H 1 + S H 2 ( S -sum), H 1 + R H 2 ( R -sum), H 1 + Q H 2 ( Q -sum), and H 1 + T H 2 ( T -sum) in the form of various indices and coindices of the factor graphs H 1 and H 2 . At the end, we have analyzed our results using numerical tables and graphical behaviour for some particular D -sum graphs.

Compound Screening with Deep Learning for Neglected Diseases: Leishmaniasis

Deep learning provides a tool for improving screening of candidates for drug re-purposing to treat neglected diseases. We show how a new pipeline can be developed to address the needs of repurposing for Leishmaniasis. In combination with traditional molecular docking techniques, this allows top candidates to be selected and analyzed, including for molecular descriptor similarity.