Covering: 2005-2025Pyrrocidines and hirsutellones are a fascinating class of fungal natural products that possess an intricate chemical structure characterized by a distinctive macrocyclic ring fused to a decahydrofluorene core. Known for their complex architectures as PKS-NRPS hybrids, these metabolites are isolated from various fungi and have attracted significant attention due to their potent pharmacological properties, including antimicrobial, antifungal, and anticancer effects. This review article aims to shed light on the structural diversities of 52 fungal-derived pyrrocidines and hirsutellones reported over the period of 2005-2025, providing insights into their chemistry, biosynthetic origins, pharmacokinetic profiles, and structure-activity relationships (SARs). Furthermore, we critically evaluate their promising therapeutic potential, highlighting the opportunities they present for modern drug discovery and development.

Cortistatins and plakinamines represent a unique class of marine-derived steroidal alkaloids, renowned for their structural diversity and potent pharmacological activities. This review provides a comprehensive overview of their chemical characteristics, pharmacological profiles, pharmacokinetics, and drug-likeness properties, with a particular focus on structure-activity relationships (SARs). Indeed, we explored their distinct molecular architectures and classification within the broader family of marine alkaloids, highlighting key subclasses and derivatives identified through advanced analytical techniques. Their broad-spectrum bioactivities, including anticancer, anti-inflammatory, antimicrobial, and antiviral effects, are discussed in detail, supported by insights into SARs and pharmacophore identification that illuminate the molecular basis of their bioactivity. Additionally, we evaluate their pharmacokinetic attributes, including absorption, distribution, metabolism, and elimination (ADME), alongside their compliance with drug-likeness criteria, offering a holistic perspective on their potential for drug development.

Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurotransmitter called dopamine interacts with the part of the brain in charge of coordination and movement. In general, the brain produces less dopamine as Parkinson's disease worsens; therefore, it becomes harder to control the movements. In this study, a dataset collected from CHEMBL library was applied to build four machine learning models using three different descriptors functions to determine the best models with the best features and suggest the best adenosine inhibitors. Molecular docking of adenosine A2A (PDB ID: 3UZA) receptor was applied to identify the potential inhibitors. The machine learning and molecular docking results indicate that XGBoost model with RDkit features is an excellent model for this dataset to explore new Anti-Parkinson's agents.

hERG channels regulate the heart's action potential by controlling potassium ion flow during repolarization. This study analyzed 6362 molecules as potential hERG inhibitors using cheminformatics and machine learning, based on the hypothesis that molecular properties influence inhibition potency. We analyzed a dataset of 6362 molecules using cheminformatics and machine learning techniques to study properties such as hydrogen bond acceptors (nHA), topological polar surface area (TPSA), molecular weight (MW) and cLogP. Principal component analysis (PCA) and scaffold visualization were used to explore the molecular diversity and identify structural motifs. We developed 14 classification structure-activity relationship (CSAR) models with the Scikit-learn package, each validated through ten rounds of cross-validation. The Random Forest model's performance was evaluated based on its accuracy across training, cross-validation and test sets. Potent hERG inhibitors typically had fewer nHAs and lower TPSA, with no consistent trend in MW. They also had slightly higher cLogP values. PCA indicated an overlap between the potent and intermediate/inactive molecules, with less diversity in the potent group. The scaffold analysis identified five main cyclic skeleton systems (CSKs) among 144. The random forest model achieved accuracies of 0.987, 0.817 and 0.747 for the training, cross-validation, and test sets, respectively, effectively predicting hERG inhibition. The study found that specific molecular properties, lower nHA and TPSA, and higher cLogP are associated with potent hERG inhibitors. Identifying common structural motifs can aid in discovering new inhibitors, and the Random Forest model proved effective in classifying compounds based on their hERG inhibition activity.

Cephalostatins and ritterazines represent fascinating classes of dimeric marine derived steroidal alkaloids with unique chemical structures and promising biological activities. Originally isolated from marine tube worms and the tunicate Ritterella tokioka collected off the coast of Japan, cephalostatins and ritterazines display potent anticancer effects by inducing apoptosis, disrupting cell cycle progression, and targeting multiple molecular pathways. This review covers the chemistry and bioactivities of 45 cephalostatins and ritterazines from 1988 to 2024, highlighting their complex structures and medicinal contributions. With insights into their structure activity relationships (SAR). Key structural elements, such as the pyrazine ring and 5/6 spiroketal moieties, are found crucial for their biological effects, suggesting interactions with lipid membranes or hydrophobic protein domains. Additionally, the formation of oxocarbenium ions from spiroketal cleavage may enhance their potency by covalently modifying DNA. The pharmacokinetics, ADMET and Drug likeness properties of these steroidal alkaloids are thoroughly addressed. Drug likeness analysis shows that these compounds fit well with the Rule of 4 (Ro4) for Protein-Protein Interaction Drugs (PPIDs), underscoring their potential in this area. Ten compounds (20, 27, 33, 34, 39, 40, 41, 42, 43, and 45) have demonstrated favourable pharmacokinetic and ADMET profiles, making them promising candidates for further research. Future efforts should focus on alternative administration routes, structural modifications, and innovative delivery systems, such as prodrugs and nanoparticles, to improve bioavailability and therapeutic effects. Advances in synthetic chemistry, mechanistic insights, and interdisciplinary collaborations will be essential for translating cephalostatins and ritterazines into effective anticancer therapies.

The heterocycle compounds, with their diverse functionalities, are particularly effective in inhibiting Janus kinases (JAKs). Therefore, it is crucial to identify the correlation between their complex structures and biological activities for the development of new drugs for the treatment of rheumatoid arthritis (RA) and cancer. In this study, a diverse set of 28 heterocyclic compounds selective for JAK1 and JAK3 was employed to construct quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR). Artificial neural network (ANN) models were employed in the development of QSAR models. The robustness and stability of the models were assessed through internal and external methodologies, including the domain of applicability (DoA). The molecular descriptors incorporated into the model exhibited a satisfactory correlation with the receptor-ligand complex structures of JAKs observed in X-ray crystallography, making the model interpretable and predictive. Furthermore, pharmacophore models ADRRR and ADHRR were designed for each JAK1 and JAK3, proving effective in discriminating between active compounds and decoys. Both models demonstrated good performance in identifying new compounds, with an ROC of 0.83 for the ADRRR model and an ROC of 0.75 for the ADHRR model. Using a pharmacophore model, the most promising compounds were selected based on their strong affinity compared to the most active compounds in the studied series each JAK1 and JAK3. Notably, the pharmacokinetic, physicochemical properties, and biological activities of the selected compounds (As compounds ZINC79189223 and ZINC66252348) were found to be consistent with their therapeutic effects in RA, owing to their non-toxic, cholinergic nature, absence of P-glycoprotein, high gastrointestinal absorption, and ability to penetrate the blood-brain barrier. Furthermore, ADMET properties were assessed, and molecular dynamics and MM/GBSA analysis revealed stability in these molecules.

BRAF kinase inhibitors have shown promise in treating melanoma patients with BRAF-V600 mutations. However, their clinical benefits are limited due to the development of acquired resistance. To address this issue, we conducted a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure–activity relationship, and landscape of human BRAF inhibitors. The final dataset comprised 3,952 molecules. Physicochemical property visualization for chemical space visualization has demonstrated that molecules from Group 1 (potent/active class) generally have slightly higher MW, RB, NumHAcceptors, and TPSA than molecules from Group 2 (intermediate/inactive class). The principal component analysis (PCA) shows that Group 1 data spreads widely and overlaps with the other group, which occupies only the left area on the plot. This suggests that molecules’ chemical structure or scaffolds are more diverse for Group 1 (Potent and Active) compounds than for Group 2 (Intermediate and Inactive) compounds. Murcko scaffold analysis has shown a greater scaffold diversity in the Active, Intermediate, and Inactive classes than in the Potent class. However, in the four bioactivity-defined classes for our BRAF dataset, molecules are uniformly distributed, with a small number of highly populated scaffolds and a large number of singletons. Furthermore, scaffold visualization has identified 12 representative Murcko scaffolds. Scaffolds 1 S1C1, S2C1, S2C2, S3C2, and S4C2 are highly favorable due to their high scaffold enrichment factor values. Based on scaffold analysis, the study investigated and summarized the local structure–activity relationships (SARs). In addition, the global SAR landscape was explored through quantitative structure–activity relationship (QSAR) modeling and structure–activity landscape visualization. Out of a total of 14 candidate models for BRAF inhibitors, a QSAR classification model that includes all 3952 molecules has been identified as the best model. The model was built using the PubChem fingerprint and extra trees algorithm and achieved an accuracy of 0.920 for the training set, 0.699 for the 10-fold cross-validation set, and 0.733 for the test set. Through a detailed analysis of the structure–activity landscapes, a total of sixteen significant consensus activity cliff (AC) generators were identified (ChEMBL molecule IDs: 4795335, 1822247, 3665863, 5083036, 5086749, 4061139, 1822242, 3665859, 1822250, 3641129, 3641043, 514688,3641178, 3697934, 3641046, and 368991). These generators offer valuable information on the SAR (Structure-Activity Relationship) for medicinal chemistry. The findings from this study provide new insights and guidelines for hit identification and lead optimization in developing novel BRAF inhibitors.

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme–inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.

The Janus kinase 3 (JAK3) family, particularly JAK3, is pivotal in initiating autoimmune diseases such as rheumatoid arthritis. Recent advancements have focused on developing antirheumatic drugs targeting JAK3, leading to the discovery of novel pyrazolopyrimidine-based compounds as potential inhibitors. This research employed covalent docking, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) analysis, molecular dynamics modeling, and MM/GBSA (Molecular Mechanics Generalized Born Surface Area) binding free energy techniques to screen 41 in silico-designed pyrazolopyrimidine derivatives. Initially, 3D structures of the JAK3 enzyme were generated using SWISS-MODEL, followed by virtual screening and covalent docking via AutoDock4 (AD4). The selection process involved the AMES test, binding affinity assessment, and ADMET analysis, narrowing down the candidates to 27 compounds that passed the toxicity test. Further covalent docking identified compounds 21 and 41 as the most promising due to their high affinity and favourable ADMET profiles. Subsequent development led to the creation of nine potent molecules, with derivatives 43 and 46 showing exceptional affinity upon evaluation through molecular dynamics simulation and MM/GBSA calculations over 300 nanoseconds, comparable to tofacitinib, an approved RA drug. However, compounds L21 and L46 demonstrated stable performance, suggesting their effectiveness in treating rheumatoid arthritis and other autoimmune conditions associated with JAK3 inhibition.

The study aimed to develop quantitative structure-toxicity relationship (QSTR) models to understand how chemical structure relates to the toxicity of 67 phenols and anilines. The chemical structures of these compounds were characterized using electronic and physico-chemical descriptors and DFT calculations were performed to obtain insights into the chemical structure and property information of the compounds. The study compared the predictive abilities of multiple linear regression (MLR), multiple nonlinear regression (MNLR), and artificial neural network (ANN) models for predicting the toxicity of the compounds. The ANN model was found to be the most effective compared the other two models. The developed models were able to accurately predict the toxicity of the compounds for four different toxicity endpoints, as demonstrated by leave-one-out cross validation, external validation, Y-randomized validation, and application domain analysis. The study suggests that the proposed descriptors could be useful in predicting the toxicity of phenols and anilines towards Chlorella vulgaris.

Objective and methods: The objective of our study is to provide forecasts on the key data of the epidemiological situation in Morocco in order to predict the number of beds in hospitals.The data sources used in this study are official and they were daily collected updated with information from the Moroccan Ministry of Health at 6:00 p.m. before the month of Ramadan and 4:00 p.m. for this month. The autoregressive integrated moving average ARIMA was applied to real-time for the two month Predictions on the Moroccan population. ARIMA models were able to estimate the number of positive cases confirmed based on two criteria. The first criterion is to determine the reliability of the statistics and the second one is to measure the accuracy of forecasting ability of the model equation. The sparse model with the lowest order of the (AR) or (MA) and (RMSE) values of the forecasts for each dataset was considered the best. Result and Conclusion: The ARIMA (1,0,0), ARIMA (9,0,0) and ARIMA (10,0,1) models were deemed to be the best suited to provide the best possible model to predict the number of positive cases for two months of prediction of the coronavirus disease 2019 (Covid-19). However, the ARIMA model (10,0,1) predicts the best model with an expected end of home confinement at the end of June 2020 with an epidemiological peak of 5000 accumulated cases caused by the coronavirus disease 2019 (Covid-19) on 13/05/2029.The models were able to predict the number of confirmed cases of the coronavirus disease 2019 (Covid-19) within a range of two months in Morocco. Thus, it can be a useful tool for health officials to improve management of the fight against the pandemic and to warn in advance of the spread of the pandemic.

In this paper, we discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. For the selection of prediction methods, we compared and explored different prediction methods. Precision, MSE and MAE were used as models due to its adaptable and probabilistic methodology on model selection. This study uses machine learning algorithms as a research method that develops models for predicting solar cell photo-current. We create an electric current prediction model. In view of the models of machine learning algorithms for example, linear regression, Lasso regression, K Nearest Neighbors, decision tree and random forest, watch their order precision execution. In this point, we recommend a solar cell photocurrent prediction model for better information based on resistance assessment. These reviews show that the linear regression algorithm, given the precision, reliably outperforms alternative models in performing the solar cell photo-current prediction Iph

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 ​+ ​G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha's. Model 34 is chosen with higher values of R2, R2 test and Q2cv (R2 ​= ​0.838, R2 test ​= ​0.735, Q2 cv ​= ​0.757). It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

BACKGROUND:Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten patients, societies and healthcare systems around the world. There is an urgent need to search for possible medications.OBJECTIVE:This article intends to use virtual screening and molecular docking methods to find potential inhibitors from existing drugs that can respond to COVID-19.METHODS:To take part in the current research investigation and to define a potential target drug that may protect the world from the pandemic of corona disease, a virtual screening study of 129 approved drugs was carried out which showed that their metabolic characteristics, dosages used, potential efficacy and side effects are clear as they have been approved for treating existing infections. Especially 12 drugs against chronic hepatitis B virus, 37 against chronic hepatitis C virus, 37 against human immunodeficiency virus, 14 anti-herpesvirus, 11 anti-influenza, and 18 other drugs currently on the market were considered for this study. These drugs were then evaluated using virtual screening and molecular docking studies on the active site of the (SARS-CoV-2) main protease (6lu7). Once the efficacy of the drug is determined, it can be approved for its in vitro and in vivo activity against the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which can be beneficial for the rapid clinical treatment of patients. These drugs were considered potentially effective against SARS-CoV-2 and those with high molecular docking scores were proposed as novel candidates for repurposing. The N3 inhibitor cocrystallized with protease (6lu7) and the anti-HIV protease inhibitor Lopinavir were used as standards for comparison.RESULTS:The results suggest the effectiveness of Beclabuvir, Nilotinib, Tirilazad, Trametinib and Glecaprevir as potent drugs against SARS-CoV-2 since they tightly bind to its main protease.CONCLUSION:These promising drugs can inhibit the replication of the virus; hence, the repurposing of these compounds is suggested for the treatment of COVID-19. No toxicity measurements are required for these drugs since they were previously tested prior to their approval by the FDA. However, the assessment of these potential inhibitors as clinical drugs requires further in vivo tests of these drugs.

Pancreatic cancer is an aggressive cancer, usually with poor prognosis, as it is mostly discovered at an advanced stage of development where treatment is challenging. Using principal components analysis (PCA) of variable selection, multiple linear regression (MLR), multiple non-linear regression (MNLR) and the artificial neural network (ANN), 2D-QSAR models for the anti-pancreatic cancer activity are developed from a set of twenty three molecules of 1,2,4-triazole derivatives to build the QSAR models. The well generated MLR and MNLR models exhibit the cross validation coefficients Q(2) of 0.51 and 0.90, respectively. Moreover, the predictive ability of those models has been evaluated by the external validation using a test set of four compounds with predicted determination coefficients R-2 test of 0.936 and 0.852, respectively. The artificial neural network (ANN) method has shown a correlation coefficient of 0.896 with an architecture 3-2-1. The obtained results indicate the validation and the good quality of the 2D-QSAR models.

In this study, essential oils (EOs) obtained from twigs, leaves and fruits of Terebinth (Pistacia terebinthus L.) was characterized by GC/MS analysis. We tested these as green corrosion inhibitors for iron in the neutral chloride medium (3% NaCl), employing electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP) curves and surface characterizations SEM, EDX, IR spectroscopy were carried out. The theoretical aspect was elaborated using molecular dynamics (MD) simulation and density functional theory (DFT). Analyses of the experimental results showed that the three main components in the EOs from the twigs, fruits and leaves of Terebinth are α-Pinene (32.65–50.58%), Limonene (6.88–15.07%), and α-Terpineol (2.50–5.15%) with quantitative variations. The fruit EO at a concentration of 3000 ppm is characterized by the best anticorrosive protective properties than the leaf and twig EOs. Indeed, the optimum percentage of this EO required to achieve the maximum efficiency was found to be 86.4% at 3000 ppm. The surface investigation strategies (SEM-EDX and IR) further validated that the corrosion barrier happens because of the adsorption of the inhibitors over the iron/3% NaCl interface. Also, the outcomes of the theoretical approach supported all the experimental results by illustrating the similar trend of inhibition efficiencies of various inhibitors and revealed that Terebinth EOs could serve as an effective inhibitor of iron in 3% NaCl.

The new coronavirus SARS-CoV-2 virus is causing a severe pneumonia in human, provoking the serious outbreak epidemic CoV-2. Since its appearance in Wuhan, China on December 2019, CoV-2 becomes the biggest challenge the world is facing today, including the discovery of antiviral drug for SARS-CoV-2. In this study, the potential inhibitory of a class of human SARS inhibitors, namely pyridine N-oxide derivatives, against CoV-2 was addressed by quantitative structure-activity relationship 3 D-QSAR. The reliable CoMSIA developed model of 110 pyridine N-oxide based-antiviral compounds, showed Q2= 0.54 and rext2=0.71. The molecular surflex-docking was applied to identify the crystal structure of CoV-2 main protease 3CLpro (PDB: 6LU7) and two potentially and largely used antiviral molecules, namely chloroquine, hydroxychloroquine. The obtained free energy affinity and ADMET properties indicate that among the series of model antiviral compounds examined, the new antiviral compound A5 could be an excellent antiviral drug inhibitor against COVID-19. The inhibition activity of pyridine N-oxyde compounds against CoV-2 was compared with the activity of two common antiviral drug, namely chloroquine (CQ) and hydroxychloroquine (HCQ). DFT method was also used to define the sites of reactivity of pyridine N-oxyde derivatives as well as CQ and HCQ.Communicated by Ramaswamy H. Sarma.

The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected whole the world with more than 6 million confirmed cases and over 370,000 deaths. At present, there are no effective treatments or vaccine for this disease, which constitutes a serious global health crisis. As the pandemic still spreading around the globe, it is of interest to use computational methods to identify potential inhibitors for the virus. The crystallographic structures of 3CLpro (PDB: 6LU7) and RdRp (PDB 6ML7) were used in virtual screening of 50000 chemical compounds obtained from the CAS Antiviral COVID19 database using 3D-similarity search and standard molecular docking followed by ranking and selection of compounds based on their binding affinity, computational techniques for the sake of details on the binding interactions, absorption, distribution, metabolism, excretion, and toxicity prediction; we report three 4-(morpholin-4-yl)-1,3,5-triazin-2-amine derivatives; two compounds (2001083-68-5 and 2001083-69-6) with optimal binding features to the active site of the main protease and one compound (833463-19-7) with optimal binding features to the active site of the polymerase for further consideration to fight COVID-19. The structural stability and dynamics of lead compounds at the active site of 3CLpro and RdRp were examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the three complexes remain stable during simulation of 20 ns, which may be suitable candidates for further experimental analysis. As the identified leads share the same scaffold, they may serve as promising leads in the development of dual 3CLpro and RdRp inhibitors against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Novel N-acylhydrazone derivatives from acridone have been synthesized by condensation of acridone acetohydrazide and various aldehyde. The novel acylhydrazones were tested for their in-vitro antibacterial activity against human pathogenic strains. The MIC results indicate that compound 3f displayed high antibacterial potential against Pseudomonas putida with MIC = 38.46 µg/mL, which is very close to that obtained with the commercial antibiotic. The synthesized compounds were subjected for docking studies to understand the interaction of our compounds and transcriptional regulator enzyme of pseudomonas putida and DNA gyrase complex of Staphyloccocus aureus.