On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research

Abstract

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

3D‐QSAR studies and molecular design on a novel series of pyrimidine benzimidazoles as Lck inhibitors

The development of selective lymphocyte‐specific kinase (Lck) inhibitors has attracted much attention for the research of the treatment of T‐cell mediated autoimmune and inflammatory diseases. In the present work, three‐dimensional quantitative structure–activity relationship (3D‐QSAR) analyses are performed on a novel series of 4‐amino‐6‐benzimidazole‐pyrimidines acting as Lck inhibitors. The established 3D‐QSAR models show significant statistical quality and satisfactory predictive ability, with high q² and R² values: the comparative molecular field analysis (CoMFA) model (q² = 0.802, R² = 0.991), and the comparative molecular similarity indexes analysis (CoMSIA) model (q² = 0.731, R² = 0.982). The systemic external validation indicates that both CoMFA and CoMSIA models are quite robust and possess high predictive abilities with values of 0.881 and 0.877, values of 0.897 and 0.847, values of 0.897 and 0.850, and values of 0.897 and 0.854, respectively. Several key structural features accounting for the inhibitory activities of these compounds are discussed. Based on established models and design considerations, six new compounds with significantly improved activities are theoretically designed, which still await experimental confirmation and evaluation. These theoretical results may provide a useful reference for understanding the action mechanism and designing novel potential Lck inhibitors. © 2014 Wiley Periodicals, Inc.     

The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study

The present study applies the Hierarchical Technology for Quantitative Structure–Activity Relationships (HiT QSAR) for (i) evaluation of the influence of the characteristics of 28 nitroaromatic compounds (some of which belong to a widely known class of explosives) as to their toxicity; (ii) prediction of toxicity for new nitroaromatic derivatives; (iii) analysis of the effects of substituents in nitroaromatic compounds on their toxicity in vivo. The 50% lethal dose concentration for rats (LD₅₀) was used to develop the QSAR models based on simplex representation of molecular structure. The preliminary 1D QSAR results show that even the information on the composition of molecules reveals the main tendencies of changes in toxicity. The statistic characteristics for partial least squares 2D QSAR models are quite satisfactory (R ² = 0.96–0.98; Q ² = 0.91–0.93; R ² _test = 0.89–0.92), which allows us to carry out the prediction of activity for 41 novel compounds designed by the application of new combinations of substituents represented in the training set. The comprehensive analysis of toxicity changes as a function of substituent position and nature was carried out. Molecular fragments that promote and interfere with toxicity were defined on the basis of the obtained models. It was shown that the mutual influence of substituents in the benzene ring plays a crucial role regarding toxicity. The influence of different substituents on toxicity can be mediated via different C–H fragments of the aromatic ring. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

Unified QSAR and network-based computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals

There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantially increases the potentialities of this kind of networks, avoiding time and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one species. To solve this problem we developed a multispecies QSAR classification model, in which the outputs were the inputs of the aforementioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extend model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power law network with few mechanisms (network hubs).     

In silico rationalization of the structural and physicochemical requirements for photobiological activity in angelicine derivatives and their heteroanalogues

In PUVA (Psoralen plus UVA) chemotherapy 8-methoxypsoralen is the most widely used compound, although its efficacy is endowed with undesired side effects. In order to have an evident anti-proliferative activity with a reduced phototoxicity, many linear and angular derivatives have been synthesised. In this paper we describe a QSAR study in which, by means of the neural networks methodology, a useful model for predicting biological activity, expressed as ID₅₀ (the UVA dose that reduces to 50% the DNA synthesis in Ehrlich cells), has been derived. A decision tree that is able to discriminate between active and inactive compounds has been built based on recursive partitioning. The study shows the key structural features responsible for the activity and could be a helpful tool in the rational design of new, less toxic, photochemotherapeuthic agents.     

QSAR,docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity

The enzyme – enoyl acyl carrier protein reductase (enoyl ACP reductase) is a validated target for antitubercular activity. Inhibition of this enzyme interferes with mycolic acid synthesis which is crucial for Mycobacterium tuberculosis cell growth. In the present work 2D and 3D quantitative structure activity relationship (QSAR) studies were carried out on a series of thiazinan–Isoniazid pharmacophore to design newer analogues. For 2D QSAR, the best statistical model was generated using SA-MLR method (r² = 0.958, q² = 0.922) while 3D QSAR model was derived using the SA KNN method (q² = 0.8498). These studies could guide the topological, electrostatic, steric, hydrophobic substitutions around the nucleus based on which the NCEs were designed. Furthermore, molecular docking was performed to gauze the binding affinity of the designed analogues for enoyl ACP reductase enzyme. Amongst all the designed analogues the binding energies of SKS 01 and SKS 05 were found to be −5.267 kcal/mol and −5.237 kcal/mol respectively which was comparable with the binding energy of the standard Isoniazid (−6.254 kcal/mol).