Current Topics in Medicinal Chemistry - Volume 23, Issue 29, 2023

Background: Nitric oxide is a free radical bioregulator controlling homeostasis, vasodilation, and inhibition of platelet aggregation, significantly implicated in the nervous and immune system functionality. In vivo it is produced by nitric oxide synthases (NOSs).Objective: Overproduction of nitric oxide is linked to several inflammatory, immunological, and neurodegenerative diseases and for that, various compounds have been synthesized as inhibitors of NOSs. In this review, the QSAR analyses were summarized in a variety of compounds as potent inhibitors of NOSs, and the models derived through 1D, 2D and 3D QSAR analyses.Conclusion: Ten groups of various NOS inhibitors and 17 1D, 2D, and 3D QSAR models and analyses were presented and discussed. A lack of hydrophobic terms was noticed in most of the cases. Chemical substituents were selected considering the increase either of the hydrophilicity and/or of hydrophobicity, bulkiness supported steric interactions, and point to potent inhibitors. CMR (Calculated Molar Refractivity) a steric variable, with a negative sign, underlines the critical effects participating on (in) an active site on the enzymes. Indicator variables imply the influence of specific structural moieties. Electronic parameters were found to be significant.

Background: Quantitative Structure-Activity Relationship (QSAR) studies describing the correlations between biological activity as dependent parameters and physicochemical and structural descriptors, including topological indices (TIs) as independent parameters, play an important role in drug discovery research. The emergence of graph theory in exploring the structural attributes of the chemical space has led to the evolution of various TIs, which have made their way into drug discovery. The TIs are easy to compute compared to the empirical parameters, but they lack physiochemical interpretation, which is essential in understanding the mechanism of action.Objectives: Hence, efforts have been made to review the work on the advances in topological indices, their physicochemical significance, and their role in developing QSAR models.Methods: A literature search has been carried out, and the research article providing evidence of the physicochemical significance of the topological parameters as well as some recent studies utilizing these parameters in the development of QSAR models, have been evaluated.Result: In this review, the physicochemical significance of TIs have been described through their correlations between empirical parameters in terms of explainable physicochemical properties, along with their application in the development of predictive QSAR models.Conclusion: Most of these findings suggest a common trend of TIs correlation with MR rather than logP or other parameters; nevertheless, the developed models may be useful in both drug and vaccine development.

Quantitative Structure-activity Relationship (QSAR) studies gained a foothold in the mid-1960s to rationalise the biological activity of medicinally important compounds. Since then, the advancements in computer hardware and software added many new techniques and areas to this field of study. Molecular dynamics (MD) simulations are one such technique in direct drug design approaches. MD simulations have a special place in drug design studies because they decode the dynamics of intermolecular interactions between a biological target and its potential ligands/inhibitors. The trajectories from MD simulations provide different non-bonding interaction parameters to assess the compatibility of the protein-ligand complex and thereby facilitate the design of prospective compounds prior to their wet-lab exploration. Histone deacetylases (HDACs) play a key role in epigenetics and they are promising drug targets for cancer and various other diseases. This review attempts to shed some light on the modelling studies of HDAC inhibitors as anticancer agents. In view of the advantages of MD simulations in direct drug design, this review also discusses the fragment-based approach in designing new inhibitors of HDAC8 and HDAC2, starting from the interaction energies of ligand fragments obtained from the MD simulations of respective protein-ligand complexes. Here, the design of new anticancer compounds from largazole thiol, trichostatin A, vorinostat, and several other prototype compounds are reviewed. These studies may stimulate the interest of medicinal chemists in MD simulations as a direct drug design approach for new drug development.

Multi-target drug development (MTDD) is the demand of the recent era, especially in the case of multi-factorial conditions such as cancer, depression, neurodegenerative diseases (NDs), etc. The MTDD approaches have many advantages; avoidance of drug-drug interactions, predictable pharmacokinetic profile, and less drug resistance. The wet lab practice in MTDD is very challenging for the researchers, and the chances of late-stage failure are obvious. Identification of an appropriate target (Target fishing) is another challenging task in the development of multi-target drugs. The in silico tools will be one of the promising tools in the MTDD for the NDs. Therefore the outlook of the review comprises a short description of NDs, target associated with different NDs, in silico studies so far done for MTDD for various NDs. The main thrust of this review is to explore the present and future aspects of in silico tools used in MTDD for different NDs in combating the challenge of drug development and the application of various in silico tools to solve the problem of target fishing.

Quantitative structure - activity relationship (QSAR) modelling is widely used in medicinal chemistry and regulatory decision making. The large amounts of data collected in recent years in materials and life sciences projects provide a solid foundation for data-driven modelling approaches that have fostered the development of machine learning and artificial intelligence tools. An overview and discussion of the principles of QSAR modelling focus on the assembly and curation of data, computation of molecular descriptor, optimization, validation, and definition of the scope of the developed QSAR models. In this review, some examples of (QSAR) models based on artificial neural networks are given to demonstrate the effectiveness of nonlinear methods for extracting information from large data sets to classify new chemicals and predict their biological properties.