Deciphering the potential of (DC.) Stapf as an anti-obesity agent: phytochemical profiling, evaluations and molecular docking studies.

Based on its anti-inflammatory and antioxidant properties, (DC) Stapf is commonly used in traditional and modern medicine to cure different diseases. The present study investigates the potential of organic extract as an anti-obesity drug in a HCHFD (high-carbohydrate, high-fat diet) model for obese rats. Its negative hypolipidemic effect has been confirmed through biochemical and histological methods.

Assessing the Potential of 1,2,3-Triazole-Dihydropyrimidinone Hybrids Against Cholinesterases: In Silico, In Vitro, and In Vivo Studies.

Combining the pharmacological properties of the 1,2,3-triazole and dihydropyrimidinone classes of compounds, two small families of mono- and di(1,2,3-triazole)-dihydropyrimidinone hybrids, A and B, were previously synthesized. The main objective of this work was to investigate the potential anti-Alzheimer effects of these hybrids. The inhibitory activities of cholinesterases (AChE and BuChE), antioxidant activity, and the inhibitory mechanism through in silico (molecular docking) and in solution (STD-NMR) experiments were evaluated.

Investigating the hepatoprotective potentiality of marine-derived steroids as promising inhibitors of liver fibrosis.

It has been reported that organic extracts derived from soft corals belonging to the genus have exhibited a wide range of therapeutic characteristics. Based on biochemical and histological techniques, we aimed to assess the hepatoprotective role of the organic extract and its principal steroidal contents derived from the Red Sea soft coral on acetaminophen-induced liver fibrosis in rats. Serum liver function parameters (ALT, AST, ALP and total bilirubin) were quantified using a spectrophotometer, and both alpha-fetoprotein (AFP) and carcinoembryonic antigen (CEA) levels were determined by using enzyme-linked immunosorbent assay (ELISA) kits while transformed growth factor beta (TGF-β) and tumor necrosis factor α (TNF-α) in liver tissue homogenate were determined using ELISA, and TGF-β and TNF-α gene expression in liver tissue was determined using real-time PCR following extraction and purification.

Marine Drug Discovery through Computer-Aided Approaches.

Besides the importance of our oceans as oxygen factories, food providers, shipping pathways, and tourism enablers, oceans hide an unprecedented wealth of opportunities [...

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation.

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications.

Investigating the antiviral therapeutic potentialities of marine polycyclic lamellarin pyrrole alkaloids as promising inhibitors for SARS-CoV-2 and Zika main proteases (Mpro).

The new coronavirus variant (SARS-CoV-2) and Zika virus are two world-wide health pandemics. Along history, natural products-based drugs have always crucially recognized as a main source of valuable medications. Considering the SARS-CoV-2 and Zika main proteases () as the re-production key element of the viral cycle and its main target, herein we report an intensive computer-aided virtual screening for a focused list of 39 marine lamellarins pyrrole alkaloids, against SARS-CoV-2 and Zika main proteases () using a set of combined modern computational methodologies including molecular docking (), molecule dynamic simulations () and structure-activity relationships () as well.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors.

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers.

Investigating the structure-activity relationship of marine polycyclic batzelladine alkaloids as promising inhibitors for SARS-CoV-2 main protease (M).

Over a span of two years ago, since the emergence of the first case of the novel coronavirus (SARS-CoV-2) in China, the pandemic has crossed borders causing serious health emergencies, immense economic crisis and impacting the daily life worldwide. Despite the discovery of numerous forms of precautionary vaccines along with other recently approved orally available drugs, yet effective antiviral therapeutics are necessarily needed to hunt this virus and its variants. Historically, naturally occurring chemicals have always been considered the primary source of beneficial medications.

Machine learning prediction of UV-Vis spectra features of organic compounds related to photoreactive potential.

Machine learning (ML) algorithms were explored for the classification of the UV-Vis absorption spectrum of organic molecules based on molecular descriptors and fingerprints generated from 2D chemical structures. Training and test data (~ 75 k molecules and associated UV-Vis data) were assembled from a database with lists of experimental absorption maxima. They were labeled with positive class (related to photoreactive potential) if an absorption maximum is reported in the range between 290 and 700 nm (UV/Vis) with molar extinction coefficient (MEC) above 1000 Lmol cm, and as negative if no such a peak is in the list.

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure-Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed.

Machine Learning Methods to Predict the Terrestrial and Marine Origin of Natural Products.

In recent years there has been a growing interest in studying the differences between the chemical and biological space represented by natural products (NPs) of terrestrial and marine origin. In order to learn more about these two chemical spaces, marine natural products (MNPs) and terrestrial natural products (TNPs), a machine learning (ML) approach was developed in the current work to predict three classes, MNPs, TNPs and a third class of NPs that appear in both the terrestrial and marine environments. In total 22,398 NPs were retrieved from the Reaxys® database, from those 10,790 molecules are recorded as MNPs, 10,857 as TNPs, and 761 NPs appear registered as both MNPs and TNPs.

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition.

The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (M) inhibitors. The CADD ligand-based method used a quantitative structure-activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data.

Antifouling Napyradiomycins from Marine-Derived Actinomycetes .

The undesired attachment of micro and macroorganisms on water-immersed surfaces, known as marine biofouling, results in severe prevention and maintenance costs (billions €/year) for aquaculture, shipping and other industries that rely on coastal and off-shore infrastructures. To date, there are no sustainable, cost-effective and environmentally safe solutions to address this challenging phenomenon. Therefore, we investigated the antifouling activity of napyradiomycin derivatives that were isolated from actinomycetes from ocean sediments collected off the Madeira Archipelago.

Intra-clade metabolomic profiling of MAR4 Streptomyces from the Macaronesia Atlantic region reveals a source of anti-biofilm metabolites.

The search for new and effective strategies to reduce bacterial biofilm formation is of utmost importance as bacterial resistance to antibiotics continues to emerge. The use of anti-biofilm agents that can disrupt recalcitrant bacterial communities can be an advantageous alternative to antimicrobials, as their use does not lead to the development of resistance mechanisms. Six MAR4 Streptomyces strains isolated from the Madeira Archipelago, at the unexplored Macaronesia Atlantic ecoregion, were used to study the chemical diversity of produced hybrid isoprenoids.

A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Infection Therapy.

The risk of methicillin-resistant (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques.

Machine learning for the prediction of molecular dipole moments obtained by density functional theory.

Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML schemes. A database was used with 10,071 structures, new molecular descriptors were designed and the models were validated with external test sets. Several ML algorithms were screened.

In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs.

To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure⁻activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R² of 0.

Computational Methodologies in the Exploration of Marine Natural Product Leads.

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty.

NavMol 3.0: enabling the representation of metabolic reactions by blind users.

Summary: The representation of metabolic reactions strongly relies on visualization, which is a major barrier for blind users. The NavMol software renders the communication and interpretation of molecular structures and reactions accessible by integrating chemoinformatics and assistive technology. NavMol 3.

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals.

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set.

The Madeira Archipelago As a Significant Source of Marine-Derived Actinomycete Diversity with Anticancer and Antimicrobial Potential.

Marine-derived actinomycetes have demonstrated an ability to produce novel compounds with medically relevant biological activity. Studying the diversity and biogeographical patterns of marine actinomycetes offers an opportunity to identify genera that are under environmental pressures, which may drive adaptations that yield specific biosynthetic capabilities. The present study describes research efforts to explore regions of the Atlantic Ocean, specifically around the Madeira Archipelago, where knowledge of the indigenous actinomycete diversity is scarce.