Band 6, Heft 7

Various computational approaches have received ever-growing role in the design of potential inhibitors of the topoisomerase 2 (TOP2A) for cancer treatment. TOP2A plays a key role in the deoxyribonucleic acid (DNA) replication before cell division and thus facilitates the growth of cells. This TOP2A function can be suppressed by targeting it with potential inhibitors in cancer cells to terminate the uncontrolled cell division. Among potential inhibitors, cryptolepine has higher selectivity along with the ability to intercalate into DNA, effectively blocking TOP2A and ceasing cell division in cancer cells. However, this compound has drawbacks of being nonspecific and possessing relatively low affinity. Therefore, a combinatorial library of 31,114 cryptolepine derivatives was designed and virtually screened by molecular docking to predict the molecular interactions between the cryptolepine derivatives and TOP2A using cryptolepine as a standard. All the binding poses of cryptolepine derivatives for TOP2A were investigated to calculate binding energy. The compounds with the database numbers 8618, 907, 147, 16755, and 8186 scored the highest binding energies, −9.88, −9.76, −9.75, −9.73, and −9.72 kcal/mol, respectively, and the highest binding affinities while the cryptolepine binding energy is −6.09 kcal/mol. The strong binding interactions of these derivatives show that they can be used as potent TOP2A inhibitors and act as more effective anticancer agents than cryptolepine itself. The interactions of these derivatives with different amino acid residues were also observed and analyzed. A comprehensive understanding of the interactions of the proposed derivatives with TOP2A helped for searching more novel and potent drug-like molecules for anticancer therapy. This computational study suggests useful references to understand inhibition mechanisms that will help in the further modifications of TOP2A inhibitors. Moreover, the DFT study of the derivatives with the highest binding energies was performed, helping to further understand the binding affinities of these compounds.

The relation that exists between the structure of a compound and its function is an integral part of chemoinformatics. The similarity principle states that “structurally similar molecules tend to have similar properties and similar molecules exert similar biological activities”. The similarity of the molecules can either be studied at the structure level or at the descriptor level (properties level). Generally, the objective of chemical similarity measures is to enhance prediction of the biological activities of molecules. In this article, an overview of various methods used to compare the similarity between metabolite structures has been provided, including two-dimensional (2D) and three-dimensional (3D) approaches. The focus has been on methods description; e.g. fingerprint-based similarity in which the molecules under study are first fragmented and their fingerprints are computed, 2D structural similarity by comparing the Tanimoto coefficients and Euclidean distances, as well as the use of physiochemical properties descriptor-based similarity methods. The similarity between molecules could also be measured by using data mining (clustering) techniques, e.g. by using virtual screening (VS)-based similarity methods. In this approach, the molecules with the desired descriptors or /and structures are screened from large databases. Lastly, SMILES-based chemical similarity search is an important method for studying the exact structure search, substructure search and also descriptor similarity. The use of a particular method depends upon the requirements of the researcher.

Carbonyl pigments are characterized by the presence of one or more carbonyl (C = O) groups in their structures, generally as a component of the chromophoric grouping and as part of an extended conjugated π-electron system. Structurally, they constitute a diverse group of pigments that offer a wide range of colors throughout the spectrum, and most of them provide high levels of technical performance. This paper provides a description of the historical development of thioindigoid, isoindoline, isoindolinone, and quinophthalone pigment types, and discusses their molecular and crystal structures in relation to their properties, the synthetic procedures used in their manufacture and their principal applications. They provide some of the most important high-performance yellow organic pigments for demanding applications in paints, inks, and plastics. Separate individual chapters in this series are devoted the anthraquinonoid, quinacridone, diketopyrrolopyrrole, perylene, and perinone carbonyl pigment subclasses.

Since their industrial introduction in the 1980s, DPP pigments now constitute a highly important group of high-performance carbonyl pigments. The DPP system was first discovered by accident in 1974, and was subsequently re-investigated by Ciba Geigy who recognized its potential to provide commercial organic pigments. DPP pigments exhibit strong similarities compared with quinacridone pigments, in terms of their molecular and crystal structures and their properties, including low solubility and excellent fastness properties. X-ray crystal structural analysis has demonstrated that their technical performance is the result of intermolecular hydrogen bonding and π–π stacking interactions in the crystal lattice structure. Based on a simple retrosynthetic analysis, an efficient synthetic process was developed by Ciba Geigy for their large-scale manufacture. DPP pigments currently provide orange through to reddish violet shades and have become of special importance in providing brilliant saturated red shades with the outstanding durability required for applications such as automotive paints.