Monte Carlo Method and GA-MLR-Based QSAR Modeling of NS5A Inhibitors against the Hepatitis C Virus.

Hepatitis C virus (HCV) is a serious disease that threatens human health. Despite consistent efforts to inhibit the virus, it has infected more than 58 million people, with 300,000 deaths per year. The HCV nonstructural protein NS5A plays a critical role in the viral life cycle, as it is a major contributor to the viral replication and assembly processes. Therefore, its importance is evident in all currently approved HCV combination treatments. The present study identifies new potential compounds for possible medical use against HCV using the quantitative structure-activity relationship (QSAR). In this context, a set of 36 NS5A inhibitors was used to build QSAR models using genetic algorithm multiple linear regression (GA-MLR) and Monte Carlo optimization and were implemented in the software CORAL. The Monte Carlo method was used to build QSAR models using SMILES-based optimal descriptors. Four splits were performed and 24 QSAR models were developed and verified through internal and external validation. The model created for split 3 produced a higher value of the determination coefficients using the validation set (R2 = 0.991 and Q2 = 0.943). In addition, this model provides interesting information about the structural features responsible for the increase and decrease of inhibitory activity, which were used to develop eight novel NS5A inhibitors. The constructed GA-MLR model with satisfactory statistical parameters (R2 = 0.915 and Q2 = 0.941) confirmed the predicted inhibitory activity for these compounds. The Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) predictions showed that the newly designed compounds were nontoxic and exhibited acceptable pharmacological properties. These results could accelerate the process of discovering new drugs against HCV.

COVID-19: In silico identification of potent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2.

The COVID-19 has been creating a global crisis, causing countless deaths and unbearable panic. Despite the progress made in the development of the vaccine, there is an urge need for the discovery of antivirals that may better work at different stages of SARS-CoV-2 reproduction. The main protease (Mpro) of the SARS-CoV-2 is a crucial therapeutic target due to its critical function in virus replication. The α-ketoamide derivatives represent an important class of inhibitors against the Mpro of the SARS-CoV. While there is 99% sequence similarity between SARS-CoV and SARS-CoV-2 main proteases, anti-SARS-CoV compounds may have a huge demonstration's prospect of their effectiveness against the SARS-CoV-2. In this study, we applied various computational approaches to investigate the inhibition potency of novel designed α-ketoamide-based compounds. In this regard, a set of 21 α-ketoamides was employed to construct a QSAR model, using the genetic algorithm-multiple linear regression (GA-MLR), as well as a pharmacophore fit model. Based on the GA-MLR model, 713 new designed molecules were reduced to 150 promising hits, which were later subject to the established pharmacophore fit model. Among the 150 compounds, the best selected compounds (3 hits) with greater pharmacophore fit score were further studied via molecular docking, molecular dynamic simulations along with the Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Our approach revealed that the three hit compounds could serve as potential inhibitors against the SARS-CoV-2 Mpro target.

Distinguishing the optimal binding mechanism of an E3 ubiquitin ligase: Covalent versus noncovalent inhibition.

The development of covalent drugs, specifically in cancer therapeutics, has recently sparked interest among the pharmaceutical research community. While representing a significant fraction of the drugs in the market, very few have been deliberately designed to interact covalently with their biological target. One of the enzymes that have been both covalently and noncovalently targeted is the Neural Precursor Cell Expressed Developmentally Downregulated gene 4-1 (Nedd4-1). This enzyme has been found to have multiple physiological implications, including its involvement in cancer invasion. A critical gap still remains in the molecular understanding of the structural mechanism upon the covalent and noncovalent binding to Nedd4-1. In this study, we explore the most optimal binding mechanism in the inhibition of the catalytic site of the Nedd4-1. Our results exhibited a greater stability in the covalent complex compared with the noncovalent complex. This was supported by the secondary structure elements that were more dominant in the covalently inhibited complex. This complex disclosed an optimal free binding energy landscape, induced by the catalytic site energy contributions that showed to be more favorable. The insights demonstrating the above binding mechanism of Nedd4-1 establishes covalent inhibition as the preferred method of inhibition of the enzyme. This investigation aids in the understanding of the structural mechanism of Nedd4-1 inhibition and would assist in the design of more potent covalent inhibitors at the catalytic site of Nedd4-1.

Tracing Potential Covalent Inhibitors of an E3 Ubiquitin Ligase through Target-Focused Modelling.

The Nedd4-1 E3 Ubiquitin ligase has been implicated in multiple disease conditions due its overexpression. Although the enzyme may be targeted both covalently and non-covalently, minimal studies provide effective inhibitors against it. Recently, research has focused on covalent inhibitors based on their characteristic, highly-selective warheads and ability to prevent drug resistance. This prompted us to screen for new covalent inhibitors of Nedd4-1 using a combination of computational approaches. However, this task proved challenging due to the limited number of electrophilic moieties available in virtual libraries. Therefore, we opted to divide an existing covalent Nedd4-1 inhibitor into two parts: a non-covalent binding group and a pre-selected α, ß-unsaturated ester that forms the covalent linkage with the protein. A non-covalent pharmacophore model was built based on molecular interactions at the binding site. The pharmacophore was then subjected to virtual screening to identify structurally similar hit compounds. Multiple filtrations were implemented prior to selecting four hits, which were validated with a covalent conjugation and later assessed by molecular dynamic simulations. The results showed that, of the four hit molecules, Zinc00937975 exhibited advantageous molecular groups, allowing for favourable interactions with one of the characteristic cysteine residues. Predictive pharmacokinetic analysis further justified the compound as a potential lead molecule, prompting its recommendation for confirmatory biological evaluation. Our inhouse, refined, pharmacophore model approach serves as a robust method that will encourage screening for novel covalent inhibitors in drug discovery.

Synthesis, characterization, X-ray, α-glucosidase inhibition and molecular docking study of new triazolic systems based on 1,5-benzodiazepine via 1,3-dipolar cycloaddition reactions.

We report in this work a synthesis of novel triazolo[1,5]benzodiazepine derivatives by the 1,3-dipolar cycloaddition reaction of N-aryl-C-ethoxycarbonylnitrilimines with 1,5-benzodiazepines. All the structures of the new compounds were determined from their NMR (1H and 13C) and HRMS. Then, X-ray crystallography analysis of compound 4d confirmed the stereochemistry of cycloadducts. The compounds 1, 4a-d, 5a-d, 6c, 7 and 8 were evaluated for their in vitro anti-diabetic activity against α-glucosidase. The compounds 1, 4d, 5a and 5b showed potential inhibitory activities compared to standard acarbose. Additionally, an in silico docking study was conducted to look into the active binding mode of the synthesized compounds within the target enzyme.Communicated by Ramaswamy H. Sarma.

Design of Potent Inhibitors Targeting the Main Protease of SARS-CoV-2 Using QSAR Modeling, Molecular Docking, and Molecular Dynamics Simulations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a serious global public health threat. The evolving strains of SARS-CoV-2 have reduced the effectiveness of vaccines. Therefore, antiviral drugs against SARS-CoV-2 are urgently needed. The main protease (Mpro) of SARS-CoV-2 is an extremely potent target due to its pivotal role in virus replication and low susceptibility to mutation. In the present study, a quantitative structure-activity relationship (QSAR) study was performed to design new molecules that might have higher inhibitory activity against SARS-CoV-2 Mpro. In this context, a set of 55 dihydrophenanthrene derivatives was used to build two 2D-QSAR models using the Monte Carlo optimization method and the Genetic Algorithm Multi-Linear Regression (GA-MLR) method. From the CORAL QSAR model outputs, the promoters responsible for the increase/decrease in inhibitory activity were extracted and interpreted. The promoters responsible for an increase in activity were added to the lead compound to design new molecules. The GA-MLR QSAR model was used to ensure the inhibitory activity of the designed molecules. For further validation, the designed molecules were subjected to molecular docking analysis and molecular dynamics simulations along with an absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. The results of this study suggest that the newly designed molecules have the potential to be developed as effective drugs against SARS-CoV-2.

Design, Synthesis, Molecular Modeling and Biological Evaluation of Novel Pyrazole Benzimidazolone Derivatives as Potent Antioxidants.

In the present study, we used benzimidazolone as a starting material to efficiently synthesize several hybrid compounds of pyrazole benzimidazolone derivatives by the 1,3-dipolar cycloaddition reaction. These compounds were obtained in average yields and were characterized by NMR (1H and 13C) and HRMS analysis. The antioxidant activity of the synthesized compounds 5(a-c) and 6(a-c) was evaluated using in vitro reduction assays, including ferric reducing antioxidant power (FRAP) and total antioxidant capacity (TAC). The results indicated that products 5c, 6b, and 6c exhibit higher antioxidant activity compared to the reference compounds and showed a remarkable ability to effectively remove the radical at IC50 (14.00 ± 0.14, 12.47± 0.02, and 12.82 ± 0.10 µM, respectively) under the TAC assessment. Conversely, compound 6c showed excellent activity at IC50 (68.97 ± 0.26 µM) in the FRAP assay. We carried out molecular docking and dynamics simulations to investigate the binding mode and stability of 5c, 6b, and 6c in the active site of human Peroxiredoxin 5. An ADMET study was conducted to determine the drug properties of the synthesized compounds.

In silico design and analysis of NS4B inhibitors against hepatitis C virus.

The hepatitis C virus is a communicable disease that gradually harms the liver leading to cirrhosis and hepatocellular carcinoma. Important therapeutic interventions have been reached since the discovery of the disease. However, its resurgence urges the need for new approaches against this malady. The NS4B receptor is one of the important proteins for Hepatitis C Virus RNA replication that acts by mediating different viral properties. In this work, we opt to explore the relationships between the molecular structures of biologically tested NS4B inhibitors and their corresponding inhibitory activities to assist the design of novel and potent NS4B inhibitors. For that, a set of 115 indol-2-ylpyridine-3-sulfonamides (IPSA) compounds with inhibitory activity against NS4B is used. A hybrid genetic algorithm combined with multiple linear regressions (GA-MLR) was implemented to construct a predictive model. This model was further used and applied to a set of compounds that were generated based on a pharmacophore modeling study combined with virtual screening to identify structurally similar lead compounds. Multiple filtrations were implemented for selecting potent hits. The selected hits exhibited advantageous molecular features, allowing for favorable inhibitory activity against HCV. The results showed that 7 out of 1285 screened compounds, were selected as potent candidate hits where Zinc14822482 exhibits the best predicted potency and pharmacophore features. The predictive pharmacokinetic analysis further justified the compounds as potential hit molecules, prompting their recommendation for a confirmatory biological evaluation. We believe that our strategy could help in the design and screening of potential inhibitors in drug discovery.Communicated by Ramaswamy H. Sarma.

Hybrid Molecules as Potential Drugs for the Treatment of HIV: Design and Applications.

Human immunodeficiency virus (HIV) infection is a major problem for humanity because HIV is constantly changing and developing resistance to current drugs. This necessitates the development of new anti-HIV drugs that take new approaches to combat an ever-evolving virus. One of the promising alternatives to combination antiretroviral therapy (cART) is the molecular hybrid strategy, in which two or more pharmacophore units of bioactive scaffolds are combined into a single molecular structure. These hybrid structures have the potential to have higher efficacy and lower toxicity than their parent molecules. Given the potential advantages of the hybrid molecular approach, the development and synthesis of these compounds are of great importance in anti-HIV drug discovery. This review focuses on the recent development of hybrid compounds targeting integrase (IN), reverse transcriptase (RT), and protease (PR) proteins and provides a brief description of their chemical structures, structure-activity relationship, and binding mode.

Host Cell Proteases Mediating SARS-CoV-2 Entry: An Overview.

The outbreak of the SARS-CoV-2 virus in late 2019 and the spread of the COVID-19 pandemic have caused severe health and socioeconomic damage worldwide. Despite the significant research effort to develop vaccines, antiviral treatments, and repurposed therapeutics to effectively contain the catastrophe, there are no available effective vaccines or antiviral drugs that can limit the threat of the disease, so the infections continue to expand. To date, the search for effective treatment remains a global challenge. Therefore, it is imperative to develop therapeutic strategies to contain the spread of SARS-CoV-2. Like other coronaviruses, SARS-CoV-2 invades and infects human host cells via the attachment of its spike envelope glycoprotein to the human host cell receptor hACE2. Subsequently, several host cell proteases facilitate viral entry via proteolytic cleavage and activation of the S protein. These host cell proteases include type II transmembrane serine proteases (TTSPs), cysteine cathepsins B and L, furin, trypsin, and Factor Xa, among others. Given the critical role of the host cell proteases in coronavirus pathogenesis, their inhibition by small molecules has successfully targeted SARS-CoV-2 in vitro, suggesting that host cell proteases are attractive therapeutic targets for SARS-CoV-2 infection. In this review, we focus on the biochemical properties of host cell proteases that facilitate the entry of SARS-CoV-2, and we highlight therapeutic small molecule candidates that have been proposed through in silico research.

Exploring the Structural Mechanism of Covalently Bound E3 Ubiquitin Ligase: Catalytic or Allosteric Inhibition?

Covalent inhibition has recently gained a resurgence of interest in several drug discovery areas. The expansion of this approach is based on evidence elucidating the selectivity and potency of covalent inhibitors when bound to particular amino acids of a biological target. The Nedd4-1, an E3 ubiquitin ligase, is characterized by two covalent binding sites, of which catalytic Cyscat and allosteric Cysallo are enclosed. This enzyme has demonstrated inhibition at both the above-mentioned binding sites; however, a detailed molecular understanding of the structural mechanism of inhibition upon Cyscat and Cysallo binding remains vague. This prompted us to provide the first account of investigating the preferential covalent binding mode and the underlying structural and molecular dynamic implications. Based on the molecular dynamic analyses, it was evident that although both catalytic and allosteric covalent binding led to greater stability of the enzyme, a preferential covalent mechanism of inhibition was seen in the allosteric-targeted system. This was supported by a more favorable binding energy in the allosteric site compared to the catalytic site, in addition to the larger number of residue interactions and stabilizing hydrogen bonds occurring in the allosteric covalent bound complex. The fundamental dynamic analysis presented in this report compliments, as well as adds to previous experimental findings, thus leading to a crucial understanding of the structural mechanism by which Nedd4-1 is inhibited. The findings from this study may assist in the design of more target-specific Nedd4-1 covalent inhibitors exploring the surface-exposed cysteine residues.

In Silico SAR Studies of HIV-1 Inhibitors.

Quantitative Structure Activity Relationships (QSAR or SAR) have helped scientists to establish mathematical relationships between molecular structures and their biological activities. In the present article, SAR studies have been carried out on 89 tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives using different classifiers, such as support vector machines, artificial neural networks, random forests, and decision trees. The goal is to propose classification models that will be able to classify TIBO compounds into two groups: high and low inhibitors of HIV-1 reverse transcriptase. Each molecular structure was encoded by 10 descriptors. To check the validity of the established models, all of them were subjected to various validation tests: internal validation, Y-randomization, and external validation. The established classification models have been successful. The correct classification rates reached 100% and 90% in the learning and test sets, respectively. Finally, molecular docking analysis was carried out to understand the interactions between reverse transcriptase enzyme and the TIBO compounds studied. Hydrophobic and hydrogen bond interactions led to the identification of active binding sites. The established models could help scientists to predict the inhibition activity of untested compounds or of novel molecules prior to their synthesis. Therefore, they could reduce the trial and error process in the design of human immunodeficiency virus (HIV) inhibitors.

Covalent Inhibition in Drug Discovery: Filling the Void in Literature.

The serendipitous discovery of covalent inhibitors and their characteristic potency of inducing irreversible and complete inhibition in therapeutic targets have caused a paradigm shift from the use of non-covalent drugs in disease treatment. This has caused a significant evolution in the field of covalent targeting to understand their inhibitory mechanisms and facilitate the systemic design of novel covalent modifiers for 'undruggable' targets. Computational techniques have evolved over the years and have significantly contributed to the process of drug discovery by mirroring the pattern of biological occurrences thereby providing insights into the dynamics and conformational transitions associated with biomolecular interactions. Moreover, our previous contributions towards the systematic design of selective covalent modifiers have revealed the various setbacks associated with the use of these conventional techniques in the study of covalent systems, hence there is a need for distinct approaches. In this review, we highlight the modifications and development of computational techniques suitable for covalent systems, their lapses, shortcomings and recent advancements.

Large gallanes and the PSEPT theory: a theoretical study of Ga(n)H(n+2) clusters (n = 7-9).

Do alanes Al(n)H(n+2) and gallanes Ga(n)H(n+2) satisfy the polyhedral skeletal electron pair theory (PSEPT)? Taking into account previous work on this question, this paper provides a convincing answer and proposes the reformulation of the (n + 1) electron pairs rule of Wade and Mingos (W-M) for such systems. Following recent studies of tetra-, penta-, hexa-, hepta-, octa-, and nonaalanes as well as valence-isoelectronic/related gallanes, in this paper we present an analysis of the hydrides of aluminum and gallium A(n)H(n+2) (A = Al, Ga and n = 7-9). The aim is still to examine the applicability of PSEPT, especially the W-M rule, to these clusters. Exploration of the total potential energy surfaces (PESs) of hepta-, octa-, and nonagallanes shows that the absolute minima have a nido-like polyhedron arrangement. Unlike the smaller Ga(n)H(n+2) (n = 4, 5, 6), it seems that the size of the cluster largely dictates its preferred geometry. Although none of them have closed (totally triangular) cages, these clusters exhibit significant compactness, comparable to borane double anions, B(n)H(n) (2-), which are the archetypes for the PSEPT theory.

The tri-mu-hydrido-bis[(eta(5)-C (5)Me (5))aluminum(III)] theoretical study, the assets of sandwiched M(2)H (3) (M of 13th group elements) stability.

The stability of the tri-mu-hydrido-bis[(eta(5)-C(5)Me(5))aluminum], Cp*(2)Al(2)H(3), 1 is studied at B3LYP/6-311+G(d,p), CCSD(T)//B3LYP/6-311+G(d,p) and MP4//B3LYP/6-311+G(d,p) levels. The coordination between Al(2)H(3) entity and both C(5)(CH(3))(5) groups is ensured by strong electrostatic and orbital interactions. The orbital analysis of the interacting fragments shows that Al(2)H(3) acceptor, which keeps its tribridged structure, implies the vacant [Formula: see text] and five antibonding ([Formula: see text], e' and e'') molecular orbitals to interact with two orbitals mixtures, b(1) and e" of the donors (C(5)Me(5)). When we take into account the solvent effect, the computation shows that 1 seems to be stable in condensed phase with a tribridged bond between the Al atoms [Cp*Al(micro-H)(3)AlCp*], whereas in the gas phase, the monobridged Cp*AlH(micro-H)AlHCp* 4 is slightly favored (4 kcal mol(-1)). We propose that 1 could be prepared thanks to Cp*Al (2) and Cp*AlH(2) (3) reaction in acidic medium. The experimental treatment of this type of metallocenes would contribute to the development of the organometallic chemistry of 13th group elements.

Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives.

The tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives, as non-nucleoside reverse transcriptase inhibitors, acquire a significant place in the treatment of the infections by the HIV. In the present paper, the support vector machines (SVM) are used to develop quantitative relationships between the anti-HIV activity and four molecular descriptors of 82 TIBO derivatives. The results obtained by SVM give good statistical results compared to those given by multiple linear regressions and artificial neural networks. The contribution of each descriptor to structure-activity relationships was evaluated. It indicates the importance of the hydrophobic parameter. The proposed method can be successfully used to predict the anti-HIV of TIBO derivatives with only four molecular descriptors which can be calculated directly from molecular structure alone.

Neural networks: Accurate nonlinear QSAR model for HEPT derivatives.

A nonlinear quantitative structure-anti-HIV-1-activity relationship (QSAR) study was investigated in a series of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine] (HEPT) derivatives acting as nonnucleoside reverse transcriptase inhibitors (NNRTIs). This QSAR study has been undertaken by a three-layered neural network (NN) using molecular descriptors known to be responsible for the anti-HIV-1 activity. The usefulness of the model and the nonlinearity of the relationship between molecular descriptors and anti-HIV-1 activity have been clearly demonstrated. The obtained model outperforms those given in the literature in both the fitting and predictive stages. NN analysis yielded predicted activities in excellent agreement with the experimentally obtained values (R(2) = 0.977, predictive r(2) = 0.862). The effect of each molecular feature on the anti-HIV-1 activity variation has been clearly elucidated.

Artificial neural networks: non-linear QSAR studies of HEPT derivatives as HIV-1 reverse transcriptase inhibitors.

Structure-anti HIV activity relationships were established for a sample of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio)thymine (HEPT) using a three-layer neural network (NN). Eight structural descriptors and physicochemical variables were used to characterize the HEPT derivatives under study. The network's architecture and parameters were optimized in order to obtain good results. All the NN architectures were able to establish a satisfactory relationship between the molecular descriptors and the anti-HIV activity. NN proved to give better results than other models in the literature. NN have been shown to be particularly successful in their ability to identify non-linear relationships.