Relevance and Potential Applications of C2-Carboxylated 1,3-Azoles.

Carbon dioxide (CO2) is an economically viable and abundant carbon source that can be incorporated into compounds such as 1,3-azoles relevant to the pharmaceutical, cosmetics, and pesticide industries. Of the 2.4 million commercially available C2-unsubstituted 1,3-azole compounds, less than 1 % are currently purchasable as their C2-carboxylated derivatives, highlighting the substantial gap in compound availability. This availability gap leaves ample opportunities for exploring the synthetic accessibility and use of carboxylated azoles in bioactive compounds. In this study, we analyze and quantify the relevance of C2-carboxylated 1,3-azoles in small-molecule research. An analysis of molecular databases such as ZINC, ChEMBL, COSMOS, and DrugBank identified relevant C2-carboxylated 1,3-azoles as anticoagulant and aroma-giving compounds. Moreover, a pharmacophore analysis highlights promising pharmaceutical potential associated with C2-carboxylated 1,3-azoles, revealing the ATP-sensitive inward rectifier potassium channel 1 (KATP) and Kinesin-like protein KIF18A as targets that can potentially be addressed with C2-carboxylated 1,3-azoles. Moreover, we identified several bioisosteres of C2-carboxylated 1,3-azoles. In conclusion, further exploration of the chemical space of C2-carboxylated 1,3-azoles is encouraged to harness their full potential in drug discovery and related fields.

Chemical space exploration with Molpher: Generating and assessing a glucocorticoid receptor ligand library.

Computational exploration of chemical space is crucial in modern cheminformatics research for accelerating the discovery of new biologically active compounds. In this study, we present a detailed analysis of the chemical library of potential glucocorticoid receptor (GR) ligands generated by the molecular generator, Molpher. To generate the targeted GR library and construct the classification models, structures from the ChEMBL database as well as from the internal IMG library, which was experimentally screened for biological activity in the primary luciferase reporter cell assay, were utilized. The composition of the targeted GR ligand library was compared with a reference library that randomly samples chemical space. A random forest model was used to determine the biological activity of ligands, incorporating its applicability domain using conformal prediction. It was demonstrated that the GR library is significantly enriched with GR ligands compared to the random library. Furthermore, a prospective analysis demonstrated that Molpher successfully designed compounds, which were subsequently experimentally confirmed to be active on the GR. A collection of 34 potential new GR ligands was also identified. Moreover, an important contribution of this study is the establishment of a comprehensive workflow for evaluating computationally generated ligands, particularly those with potential activity against targets that are challenging to dock.

Using the High-Entropy Approach to Obtain Multimetal Oxide Nanozymes: Library Synthesis, In Silico Structure-Activity, and Immunoassay Performance.

High-entropy nanomaterials exhibit exceptional mechanical, physical, and chemical properties, finding applications in many industries. Peroxidases are metalloenzymes that accelerate the decomposition of hydrogen peroxide. This study uses the high-entropy approach to generate multimetal oxide-based nanozymes with peroxidase-like activity and explores their application as sensors in ex vivo bioassays. A library of 81 materials was produced using a coprecipitation method for rapid synthesis of up to 100 variants in a single plate. The A and B sites of the magnetite structure, (AA')(BB'B'')2O4, were substituted with up to six different cations (Cu/Fe/Zn/Mg/Mn/Cr). Increasing the compositional complexity improved the catalytic performance; however, substitutions of single elements also caused drastic reductions in the peroxidase-like activity. A generalized linear model was developed describing the relationship between material composition and catalytic activity. Binary interactions between elements that acted synergistically or antagonistically were identified, and a single parameter, the mean interaction effect, was observed to correlate highly with catalytic activity, providing a valuable tool for the design of high-entropy-inspired nanozymes.

Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning.

Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These knowledge-sharing approaches encompass transfer learning, multitask learning, and meta-learning. A key question remaining to be answered for these approaches is about the extent to which their performance can benefit from the relatedness of available source (training) tasks; in other words, how difficult ("hard") a test task is to a model, given the available source tasks. This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning on the FS-Mol data set, we demonstrate that the proposed task hardness metric is inversely correlated with performance (Pearson's correlation coefficient r = -0.72). The metric will be useful in estimating the task-specific gain in performance that can be achieved through meta-learning.

A mini-review of the anti-SARS-CoV-2 potency of Amaryllidaceae alkaloids.

BACKGROUND: Nature has perennially served as an infinite reservoir of diverse chemicals with numerous applications benefiting humankind. In recent years, due to the emerging COVID-19 pandemic, there has been a surge in studies on repurposing natural products as anti-SARS-CoV-2 agents, including plant-derived substances. Among all types of natural products, alkaloids remain one of the most important groups with various known medicinal values. The current investigation focuses on Amaryllidaceae alkaloids (AAs) since AAs have drawn significant scientific attention as anti-SARS-CoV-2 agents over the past few years. PURPOSE AND STUDY DESIGN: This study serves as a mini-review, summarizing recent advances in studying the anti-SARS-CoV-2 potency of AAs, covering two aspects: structure-activity relationship and mechanism of action (MOA). METHODS: The study covers the period from 2019 to 2023. The information in this review were retrieved from common databases including Web of Science, ScienceDirect, PubMed and Google scholar. Reported anti-SARS-CoV-2 potency, cytotoxicity and possible biological targets of AAs were summarized and classified into different skeletal subclasses. Then, the structure-activity relationship (SAR) was explored, pinpointing the key pharmacophore-related structural moieties. To study the mechanism of action of anti-SARS-CoV-2 AAs, possible biological targets were discussed. RESULTS: In total, fourteen research articles about anti-SARS-CoV-2 was selected. From the SAR point of view, four skeletal subclasses of AAs (lycorine-, galanthamine-, crinine- and homolycorine-types) appear to be promising for further investigation as anti-SARS-CoV-2 agents despite experimental inconsistencies in determining in vitro half maximal inhibitory effective concentration (EC50). Narciclasine, haemanthamine- and montanine-type skeletons were cytotoxic and devoid of anti-SARS-CoV-2 activity. The lycorine-type scaffold was the most structurally diverse in this study and preliminary structure-activity relationships revealed the crucial role of ring C and substituents on rings A, C and D in its anti-SARS-CoV-2 activity. It also appears that two enantiomeric skeletons (haemanthamine- and crinine-types) displayed opposite activity/toxicity profiles regarding anti-SARS-CoV-2 activity. Pharmacophore-related moieties of the haemanthamine/crinine-type skeletons were the substituents on rings B, C and the dioxymethylene moiety. All galanthamine-type alkaloids in this study were devoid of cytotoxicity and it appears that varying substituents on rings C and D could enhance the anti-SARS-CoV-2 potency. Regarding MOAs, initial experimental results suggested Mpro and RdRp as possible viral targets. Dual functionality between anti-inflammatory activity on host cells and anti-SARS-CoV-2 activity on the SARS-CoV-2 virus of isoquinoline alkaloids, including AAs, were suggested as the possible MOAs to alleviate severe complications in COVID-19 patients. This dual functionality was proposed to be related to the p38 MAPK signaling pathway. CONCLUSION: Overall, Amaryllidaceae alkaloids appear to be promising for further investigation as anti-SARS-CoV-2 agents. The skeletal subclasses holding the premise for further investigation are lycorine-, crinine-, galanthamine- and homolycorine-types.

Tackling assay interference associated with small molecules.

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Targeted metabolite profiling of Salvia rosmarinus Italian local ecotypes and cultivars and inhibitory activity against Pectobacterium carotovorum subsp. carotovorum.

Introduction: The development of agriculture in terms of sustainability and low environmental impact is, at present, a great challenge, mainly in underdeveloped and marginal geographical areas. The Salvia rosmarinus "Eretto Liguria" ecotype is widespread in Liguria (Northwest Italy), and farmers commonly use it by for cuttings and for marketing. In the present study, this ecotype was characterized in comparison with other cultivars from the same geographical region and Campania (Southern Italy), with a view to application and registration processes for the designation of protected geographical indications. Moreover, the possibility of using the resulting biomass after removing cuttings or fronds as a source of extracts and pure compounds to be used as phytosanitary products in organic farming was evaluated. Specifically, the potential of rosemary extracts and pure compounds to prevent soft rot damage was then tested. Methods: A targeted NMR metabolomic approach was employed, followed by multivariate analysis, to characterize the rosemary accessions. Bacterial soft rot assay and disk diffusion test were carried out to evaluate the activity of extracts and isolated compounds against Pectobacterium carotovorum subsp. carotovorum. Enzymatic assay was performed to measure the in vitro inhibition of the pectinase activity produced by the selected pathogen. Molecular docking simulations were used to explore the possible interaction of the selected compounds with the pectinase enzymes. Results and Discussion: The targeted metabolomic analysis highlighted those different geographical locations can influence the composition and abundance of bioactive metabolites in rosemary extracts. At the same time, genetic factors are important when a single geographical area is considered. Self-organizing maps (SOMs) showed that the accessions of "Eretto Liguria" appeared well characterized when compared to the others and had a good content in specialized metabolites, particularly carnosic acid. Soft rotting Enterobacteriaceae belonging to the Pectobacterium genus represent a serious problem in potato culture. Even though rosemary methanolic extracts showed a low antibacterial activity against a strain of Pectobacterium carotovorum subsp. carotovorum in the disk diffusion test, they showed ability in reducing the soft rot damage induced by the bacterium on potato tissue. 7-O-methylrosmanol, carnosol and isorosmanol appeared to be the most active components. In silico studies indicated that these abietane diterpenoids may interact with P. carotovorum subsp. carotovorum pectate lyase 1 and endo-polygalacturonase, thus highlighting these rosemary components as starting points for the development of agents able to prevent soft rot progression.

Epstein-Barr virus-driven B cell lymphoma mediated by a direct LMP1-TRAF6 complex.

Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) drives viral B cell transformation and oncogenesis. LMP1's transforming activity depends on its C-terminal activation region 2 (CTAR2), which induces NF-κB and JNK by engaging TNF receptor-associated factor 6 (TRAF6). The mechanism of TRAF6 recruitment to LMP1 and its role in LMP1 signalling remains elusive. Here we demonstrate that TRAF6 interacts directly with a viral TRAF6 binding motif within CTAR2. Functional and NMR studies supported by molecular modeling provide insight into the architecture of the LMP1-TRAF6 complex, which differs from that of CD40-TRAF6. The direct recruitment of TRAF6 to LMP1 is essential for NF-κB activation by CTAR2 and the survival of LMP1-driven lymphoma. Disruption of the LMP1-TRAF6 complex by inhibitory peptides interferes with the survival of EBV-transformed B cells. In this work, we identify LMP1-TRAF6 as a critical virus-host interface and validate this interaction as a potential therapeutic target in EBV-associated cancer.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.