Insights into activity enhancement of 4-aminoquinoline-based hybrids using atom-based and field-based QSAR studies

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Machine learning (ML) methods have attracted increasing interest in chemistry as in all fields of science in recent years. This method is of great importance for the design of targeted bioactive compounds, especially by avoiding loss of time, money, and chemicals. There are lots of online web-based platforms such as LibSVM and OCHEM for the application of ML methods. In this paper, it has been examined the literature data on the activity predictions of heterocyclic compounds, biological activity results such as antiurease, HIV-1 Integrase, E. Coli DNA Gyrase B, and antifungal, pharmacophore-based studies, synthesis, and finding possible inhibitors using different machine learning methods.

Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations

Sirtuin 2 (Sirt2) nicotinamide adenine dinucleotide-dependent deacetylase enzyme has been reported to alter diverse biological functions in the cells and onset of diseases, including cancer, aging, and neurodegenerative diseases, which implicate the regulation of Sirt2 function as a potential drug target. Available Sirt2 inhibitors or modulators exhibit insufficient specificity and potency, and even partially contradictory Sirt2 effects were described for the available inhibitors. Herein, we applied computational screening and evaluation of FDA-approved drugs for highly selective modulation of Sirt2 activity via a unique inhibitory mechanism as reported earlier for SirReal2 inhibitor. Application of stringent molecular docking results in the identification of 48 FDA-approved drugs as selective putative inhibitors of Sirt2, but only top 10 drugs with docking scores > - 11 kcal/mol were considered in reference to SirReal2 inhibitor for computational analysis. The molecular dynamics simulations and post-simulation analysis of Sirt2-drug complexes revealed substantial stability for Fluphenazine and Nintedanib with Sirt2. Additionally, developed 3D-QSAR-models also support the inhibitory potential of drugs, which exclusively revealed highest activities for Nintedanib (pIC50 ≥ 5.90 µM). Conclusively, screened FDA-approved drugs were advocated as promising agents for Sirt2 inhibition and required in vitro investigation for Sirt2 targeted drug development.

Recent advances in quantitative structure-activity relationship models of antimalarial drugs

INTRODUCTION

Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure-activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates.

AREAS COVERED

The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015-2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015-2019.

EXPERT OPINION

Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.

Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models

The Malaria burden was an escalating global encumbrance and need to be addressed with critical care. Anti-malarial drug discovery was integrated with supervised machine learning (ML) models to identify potent thiazolyl-traizine derivatives. This assimilated approach of Direct Kernel-based Partial Least Squares regression (DKPLS) with molprint 2D fingerprints in Quantitative Structure Activity Relationship models was utilized to map the knowledge of known actives and to design novel molecules. This QSAR study had revealed the structural features required for better antimalarial activity. Two of the molecules which were designed based on the results of this QSAR study, had shown good percentage of parasitemia against both chloroquine sensitive (3D7) and chloroquine resistant (Dd2) strains of Plasmodium falciparum respectively. The IC₅₀ of 201D and 204D was 3.02 and 2.17 µM against chloroquine resistant Dd2 strain of Plasmodium falciparum. This result had proved the efficiency of a multidisciplinary approach of medicinal chemistry and machine learning for the design of novel potent anti-malarial compounds.

Hybrid molecules with potential in vitro antiplasmodial and in vivo antimalarial activity against drug-resistant Plasmodium falciparum

Malaria is a tropical disease, leading to around half a million deaths annually. Antimalarials such as quinolines are crucial to fight against malaria, but malaria control is extremely challenged by the limited pipeline of effective pharmaceuticals against drug-resistant strains of Plasmodium falciparum which are resistant toward almost all currently accessible antimalarials. To tackle the growing resistance, new antimalarial drugs are needed urgently. Hybrid molecules which contain two or more pharmacophores have the potential to overcome the drug resistance, and hybridization of quinoline privileged antimalarial building block with other antimalarial pharmacophores may provide novel molecules with enhanced in vitro and in vivo activity against drug-resistant (including multidrug-resistant) P falciparum. In recent years, numerous of quinoline hybrids were developed, and their activities against a panel of drug-resistant P falciparum strains were screened. Some of quinoline hybrids were found to possess promising in vitro and in vivo potency. This review emphasized quinoline hybrid molecules with potential in vitro antiplasmodial and in vivo antimalarial activity against drug-resistant P falciparum, covering articles published between 2010 and 2019.

Triazole derivatives and their antiplasmodial and antimalarial activities

Malaria, caused by protozoan parasites of the genus Plasmodium especially by the most prevalent parasite Plasmodium falciparum, represents one of the most devastating and common infectious disease globally. Nearly half of the world population is under the risk of being infected, and more than 200 million new clinical cases with around half a million deaths occur annually. Drug therapy is the mainstay of antimalarial therapy, yet current drugs are threatened by the development of resistance, so it's imperative to develop new antimalarials with great potency against both drug-susceptible and drug-resistant malaria. Triazoles, bearing a five-membered heterocyclic ring with three nitrogen atoms, exhibit promising in vitro antiplasmodial and in vivo antimalarial activities. Moreover, several triazole-based drugs have already used in clinics for the treatment of various diseases, demonstrating the excellent pharmaceutical profiles. Therefore, triazole derivatives have the potential for clinical deployment in the control and eradication of malaria. This review covers the recent advances of triazole derivatives especially triazole hybrids as potential antimalarials. The structure-activity relationship is also discussed to provide an insight for rational designs of more efficient antimalarial candidates.

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).