Green banana (Musa ssp.) mixed pulp and peel flour: A new ingredient with interesting bioactive, nutritional, and technological properties for food applications.

This study aimed to characterize and evaluate the in vitro bioactive properties of green banana pulp (GBPF), peel (GBPeF), and mixed pulp/peel flours M1 (90/10) and M2 (80/20). Lipid concentration was higher in GBPeF (7.53%), as were the levels of free and bound phenolics (577 and 653.1 mg GAE/100 g, respectively), whereas the resistant starch content was higher in GBPF (44.11%). Incorporating up to 20% GBPeF into the mixed flour had a minor effect on the starch pasting properties of GBPF. GBPeF featured rutin and trans-ferulic acid as the predominant free and bound phenolic compounds, respectively. GBPF presented different major free phenolics, though it had similar bound phenolics to GBPeF. Both M1 and M2 demonstrated a reduction in intracellular reactive oxygen species (ROS) generation. Consequently, this study validates the potential of green banana mixed flour, containing up to 20% GBPeF, for developing healthy foods and reducing post-harvest losses.

Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus.

The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 µM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach.

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 µM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 µM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

New anti-SARS-CoV-2 aminoadamantane compounds as antiviral candidates for the treatment of COVID-19.

Here, the antiviral activity of aminoadamantane derivatives were evaluated against SARS-CoV-2. The compounds exhibited low cytotoxicity to Vero, HEK293 and CALU-3 cells up to a concentration of 1,000 µM. The inhibitory concentration (IC50) of aminoadamantane was 39.71 µM in Vero CCL-81 cells and the derivatives showed significantly lower IC50 values, especially for compounds 3F4 (0.32 µM), 3F5 (0.44 µM) and 3E10 (1.28 µM). Additionally, derivatives 3F5 and 3E10 statistically reduced the fluorescence intensity of SARS-CoV-2 protein S from Vero cells at 10 µM. Transmission microscopy confirmed the antiviral activity of the compounds, which reduced cytopathic effects induced by the virus, such as vacuolization, cytoplasmic projections, and the presence of myelin figures derived from cellular activation in the face of infection. Additionally, it was possible to observe a reduction of viral particles adhered to the cell membrane and inside several viral factories, especially after treatment with 3F4. Moreover, although docking analysis showed favorable interactions in the catalytic site of Cathepsin L, the enzymatic activity of this enzyme was not inhibited significantly in vitro. The new derivatives displayed lower predicted toxicities than aminoadamantane, which was observed for either rat or mouse models. Lastly, in vivo antiviral assays of aminoadamantane derivatives in BALB/cJ mice after challenge with the mouse-adapted strain of SARS-CoV-2, corroborated the robust antiviral activity of 3F4 derivative, which was higher than aminoadamantane and its other derivatives. Therefore, aminoadamantane derivatives show potential broad-spectrum antiviral activity, which may contribute to COVID-19 treatment in the face of emerging and re-emerging SARS-CoV-2 variants of concern.

Overcoming class imbalance in drug discovery problems: Graph neural networks and balancing approaches.

This research investigates the application of Graph Neural Networks (GNNs) to enhance the cost-effectiveness of drug development, addressing the limitations of cost and time. Class imbalances within classification datasets, such as the discrepancy between active and inactive compounds, give rise to difficulties that can be resolved through strategies like oversampling, undersampling, and manipulation of the loss function. A comparison is conducted between three distinct datasets using three different GNN architectures. This benchmarking research can steer future investigations and enhance the efficacy of GNNs in drug discovery and design. Three hundred models for each combination of architecture and dataset were trained using hyperparameter tuning techniques and evaluated using a range of metrics. Notably, the oversampling technique outperforms eight experiments, showcasing its potential. While balancing techniques boost imbalanced dataset models, their efficacy depends on dataset specifics and problem type. Although oversampling aids molecular graph datasets, more research is needed to optimize its usage and explore other class imbalance solutions.

Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches.

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC50 values of 29.4 and 2.9 µM, respectively, while the standard drug benznidazole displayed IC50 values of 17.7 and 5.0 µM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC50 = 92.8 µM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.

Discovery of Lead 2-Thiazolylhydrazones with Broad-Spectrum and Potent Antifungal Activity.

Opportunistic fungal infections represent a global health problem, mainly for immunocompromised individuals. New therapeutical options are needed since several fungal strains show resistance to clinically available antifungal agents. 2-Thiazolylhydrazones are well-known as potent compounds against Candida and Cryptococcus species. A scaffold-focused drug design using machine-learning models was established to optimize the 2-thiazolylhydrazone skeleton and obtain novel compounds with higher potency, better solubility in water, and enhanced absorption. Twenty-nine novel compounds were obtained and most showed low micromolar MIC values against different species of Candida and Cryptococcus spp., including Candida auris, an emerging multidrug-resistant yeast. Among the synthesized compounds, 2-thiazolylhydrazone 28 (MIC value ranging from 0.8 to 52.17 µM) was selected for further studies: cytotoxicity evaluation, permeability study in Caco-2 cell model, and in vivo efficacy against Cryptococcus neoformans in an invertebrate infection model. All results obtained indicate the great potential of 28 as a novel antifungal agent.

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling.

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

Structure-based discovery of thiosemicarbazones as SARS-CoV-2 main protease inhibitors.

Aim: Discovery of novel SARS-CoV-2 main protease (Mpro) inhibitors using a structure-based drug discovery strategy. Materials & methods: Virtual screening employing covalent and noncovalent docking was performed to discover Mpro inhibitors, which were subsequently evaluated in biochemical and cellular assays. Results: 91 virtual hits were selected for biochemical assays, and four were confirmed as reversible inhibitors of SARS CoV-2 Mpro with IC50 values of 0.4-3 µM. They were also shown to inhibit SARS-CoV-1 Mpro and human cathepsin L. Molecular dynamics simulations indicated the stability of the Mpro inhibitor complexes and the interaction of ligands at the subsites. Conclusion: This approach led to the discovery of novel thiosemicarbazones as potent SARS-CoV-2 Mpro inhibitors.

Trypanosoma cruzi Sirtuin 2 as a Relevant Druggable Target: New Inhibitors Developed by Computer-Aided Drug Design.

Trypanosoma cruzi, the etiological agent of Chagas disease, relies on finely coordinated epigenetic regulation during the transition between hosts. Herein we targeted the silent information regulator 2 (Sir2) enzyme, a NAD+-dependent class III histone deacetylase, to interfere with the parasites' cell cycle. A combination of molecular modelling with on-target experimental validation was used to discover new inhibitors from commercially available compound libraries. We selected six inhibitors from the virtual screening, which were validated on the recombinant Sir2 enzyme. The most potent inhibitor (CDMS-01, IC50 = 40 µM) was chosen as a potential lead compound.

Isolation and Characterization of the Arapaima gigas Growth Hormone (ag-GH) cDNA and Three-Dimensional Modeling of This Hormone in Comparison with the Human Hormone (hGH).

In a previous work, the common gonadotrophic hormone α-subunit (ag-GTHα), the ag-FSH ß- and ag-LH ß-subunit cDNAs, were isolated and characterized by our research group from A. gigas pituitaries, while a preliminary synthesis of ag-FSH was also carried out in human embryonic kidney 293 (HEK293) cells. In the present work, the cDNA sequence encoding the ag-growth hormone (ag-GH) has also been isolated from the same giant Arapaimidae Amazonian fish. The ag-GH consists of 208 amino acids with a putative 23 amino acid signal peptide and a 185 amino acid mature peptide. The highest identity, based on the amino acid sequences, was found with the Elopiformes (82.0%), followed by Anguilliformes (79.7%) and Acipenseriformes (74.5%). The identity with the corresponding human GH (hGH) amino acid sequence is remarkable (44.8%), and the two disulfide bonds present in both sequences were perfectly conserved. Three-dimensional (3D) models of ag-GH, in comparison with hGH, were generated using the threading modeling method followed by molecular dynamics. Our simulations suggest that the two proteins have similar structural properties without major conformational changes under the simulated conditions, even though they are separated from each other by a >100 Myr evolutionary period (1 Myr = 1 million years). The sequence found will be used for the biotechnological synthesis of ag-GH while the ag-GH cDNA obtained will be utilized for preliminary Gene Therapy studies.

Do Go Chasing Waterfalls: Enoyl Reductase (FabI) in Complex with Inhibitors Stabilizes the Tetrameric Structure and Opens Water Channels.

The enzyme enoyl-ACP reductase (FabI) is the limiting step of the membrane's fatty acid biosynthesis in bacteria and a druggable target for novel antibacterial agents. The FabI active form is a homotetramer, which displays the highest affinity to inhibitors. Herein, molecular dynamics studies were carried out using the structure of FabI in complex with known inhibitors to investigate their effects on tetramerization. Our results suggest that multimerization is essential for the integrity of the catalytic site and that inhibitor binding enables the multimerization by stabilizing the substrate binding loop (SBL, L:195-200) coupled with changes in the H4/5 (QR interface). We also observed that AFN-1252 (naphtpyridinone derivative) promotes unique conformational changes affecting monomer-monomer interfaces. These changes are induced by AFN-1252 interaction with key residues in the binding sites (Ala95, Tyr146, and Tyr156). In addition, the analysis of water trajectories indicated that AFN-1252 complexes allow more water molecules to enter the binding site than triclosan and MUT056399 complexes. FabI-AFN-1252 simulations show accumulation of water molecules near the Tyr146/147 pocket, which can become a hotspot to the design of novel FabI inhibitors.

Effects of Lipophilicity and Structural Features on the Antiherpes Activity of Digitalis Cardenolides and Derivatives.

There is growing interest in exploring Digitalis cardenolides as potential antiviral agents. Hence, we herein investigated the influence of structural features and lipophilicity on the antiherpes activity of 65 natural and semisynthetic cardenolides assayed in vitro against HSV-1. The presence of an α,ß-unsaturated lactone ring at C-17, a ß-hydroxy group at C-14 and C-3ß-OR substituents were considered essential requirements for this biological activity. Glycosides were more active than their genins, especially monoglycosides containing a rhamnose residue. The activity enhanced in derivatives bearing an aldehyde group at C-19 instead of a methyl group, whereas inserting a C-5ß-OH improved the antiherpes effect significantly. The cardenolides lipophilicity was accessed by measuring experimentally their log P values (n-octanol-water partition coefficient) and disclosed a range of lipophilicity (log P 0.75±0.25) associated with the optimal antiherpes activity. In silico studies were carried out and resulted in the establishment of two predictive models potentially useful to identify and/or optimize novel antiherpes cardenolides. The effectiveness of the models was confirmed by retrospective analysis of the studied compounds. This is the first SAR study addressing the antiherpes activity of cardenolides. The developed computational models were able to predict the active cardenolides and their log P values.

Black tea kombucha: Physicochemical, microbiological and comprehensive phenolic profile changes during fermentation, and antimalarial activity.

This study shows the changes in physicochemical and microbiological composition, and in the phenolic profile of black tea kombucha during fermentation. In addition, the antimalarial potential of the kombucha was evaluated. Ultra-performance liquid chromatography-mass spectrometry multiplex analysis (UPLC-MSE) results revealed a 1.7 log2 fold-change increase in phenolics with the fermentation time, with emphasis on the increase of phenolic acids (0.3 log2 fold-change). Over time there was degradation of flavonoids such as nepetin, hesperidin and catechin 5-O-gallate, to the detriment of the increase in phenolic acids such as gallic acid and cinnamic acid. In addition, black tea kombucha presented antiplasmodic activity against the 3D7 (sensitive chloroquine) and W2 (resistant to chloroquine) strains. Therefore, important changes in the black tea kombucha phenolic profile take place during fermentation, which may help in the development of kombuchas with higher bioactive potential and contribute to a better understanding of the kombucha fermentation process.

The Brazilian compound library (BraCoLi) database: a repository of chemical and biological information for drug design.

The Brazilian Compound Library (BraCoLi) is a novel open access and manually curated electronic library of compounds developed by Brazilian research groups to support further computer-aided drug design works, available on https://www.farmacia.ufmg.br/qf/downloads/ . Herein, the first version of the database is described comprising 1176 compounds. Also, the chemical diversity and drug-like profiles of BraCoLi were defined to analyze its chemical space. A significant amount of the compounds fitted Lipinski and Veber's rules, alongside other drug-likeness properties. A comparison using principal component analysis showed that BraCoLi is similar to other databases (FDA-approved drugs and NuBBEDB) regarding structural and physicochemical patterns. Furthermore, a scaffold analysis showed that BraCoLi presents several privileged chemical skeletons with great diversity. Despite the similar distribution in the structural and physicochemical spaces, Tanimoto coefficient values indicated that compounds present in the BraCoLi are generally different from the two other databases, where they showed different kernel distributions and low similarity. These facts show an interesting innovative aspect, which is a desirable feature for novel drug design purposes.

Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity.

Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r2 for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.

Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade.

Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.

The application of machine learning techniques to innovative antibacterial discovery and development.

INTRODUCTION: After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing prevalence of antibiotic drug resistance. However, the increasing amount of available data promotes the use of machine learning techniques (MLT) in drug discovery projects (e.g. construction of regression/classification models and ranking/virtual screening of compounds). AREAS COVERED: In this review, the authors cover some of the applications of MLT in medicinal chemistry, focusing on the development of new antibiotics, the prediction of resistance and its mechanisms. The aim of this review is to illustrate the main advantages and disadvantages and the major trends from studies over the past 5 years. EXPERT OPINION: The application of MLT to antibacterial drug discovery can aid the selection of new and potent lead compounds, with desirable pharmacokinetic and toxic profiles for further optimization. The increasing volume of available data along with the constant improvement in computational power and algorithms has meant that we are experiencing a transition in the way we face modern issues such as drug resistance, where our decisions are data-driven and experiments can be focused by data-suggested hypotheses.

QSAR studies on the human sirtuin 2 inhibition by non-covalent 7,5,2-anilinobenzamide derivatives.

Sirtuin 2 is a key enzyme in gene expression regulation that is often associated with tumor proliferation control and therefore is a relevant anticancer drug target. Anilinobenzamide derivatives have been discussed as selective sirtuin 2 inhibitors and can be developed further. In the present study, hologram and three-dimensional quantitative structure-activity relationship (HQSAR and 3D-QSAR) analyses were employed for determining structural contributions of a compound series containing human sirtuin-2-selective inhibitors that were then correlated with structural data from the literature. The final QSAR models were robust and predictive according to statistical validation (q2 and r2pred values higher than 0.85 and 0.75, respectively) and could be employed further to generate fragment contribution and contour maps. 3D-QSAR models together with information about the chemical properties of sirtuin 2 inhibitors can be useful for designing novel bioactive ligands.Communicated by Ramaswamy H. Sarma.