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Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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