Increasing Accessibility of Bayesian Network-Based Defined Approaches for Skin Sensitisation Potency Assessment.

Skin sensitisation is a critical adverse effect assessed to ensure the safety of compounds and materials exposed to the skin. Alongside the development of new approach methodologies (NAMs), defined approaches (DAs) have been established to promote skin sensitisation potency assessment by adopting and integrating standardised in vitro, in chemico, and in silico methods with specified data analysis procedures to achieve reliable and reproducible predictions. The incorporation of additional NAMs could help increase accessibility and flexibility.

Deciphering Molecular Embeddings with Centered Kernel Alignment.

Analyzing machine learning models, especially nonlinear ones, poses significant challenges. In this context, centered kernel alignment (CKA) has emerged as a promising model analysis tool that assesses the similarity between two embeddings. CKA's efficacy depends on selecting a kernel that adequately captures the underlying properties of the compared models.

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

Carbon dioxide (CO) is an economically viable and abundant carbon source that can be incorporated into compounds such as C2-carboxylated 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.

Using the High-Entropy Approach to Obtain Multimetal Oxide Nanozymes: Library Synthesis, 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 bioassays.

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.

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.

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.

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.

Targeted metabolite profiling of Italian local ecotypes and cultivars and inhibitory activity against subsp. .

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 "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.

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.

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.

School of cheminformatics in Latin America.

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting.

Flindissone, a Limonoid Isolated from , Is an LXR Agonist.

In this study, the ability of six limonoids from (Meliaceae) to activate the liver X receptor (LXR) was assessed. One of these limonoids, flindissone, was shown to activate LXR by reporter-gene assays. Flindissone is a ring-intact limonoid, structurally similar to sterol-like LXR ligands.

Computational prediction of the metabolites of agrochemicals formed in rats.

Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats.

Toxicity prediction using target, interactome, and pathway profiles as descriptors.

In silico methods are essential to the safety evaluation of chemicals. Computational risk assessment offers several approaches, with data science and knowledge-based methods becoming an increasingly important sub-group. One of the substantial attributes of data science is that it allows using existing data to find correlations, build strong hypotheses, and create new, valuable knowledge that may help to reduce the number of resource intensive experiments.

Structural Insights into the Ligand-LsrK Kinase Binding Mode: A Step Forward in the Discovery of Novel Antimicrobial Agents.

LsrK is a bacterial kinase that triggers the quorum sensing, and it represents a druggable target for the identification of new agents for fighting antimicrobial resistance. Herein, we exploited tryptophan fluorescence spectroscopy (TFS) as a suitable technique for the identification of potential LsrK ligands from an in-house library of chemicals comprising synthetic compounds as well as secondary metabolites. Three secondary metabolites (, and ) showed effective binding to LsrK, with KD values in the sub-micromolar range.

Identification of Natural Products Inhibiting SARS-CoV-2 by Targeting Viral Proteases: A Combined in Silico and in Vitro Approach.

In this study, an integrated in silico-in vitro approach was employed to discover natural products (NPs) active against SARS-CoV-2. The two SARS-CoV-2 viral proteases, i.e.

Discovery of -quinazolinone-4-hydroxy-2-quinolone-3-carboxamides as DNA gyrase B-targeted antibacterial agents.

Emerging drug resistance is generating an urgent need for novel and effective antibiotics. A promising target that has not yet been addressed by approved antibiotics is the bacterial DNA gyrase subunit B (GyrB), and GyrB inhibitors could be effective against drug-resistant bacteria, such as methicillin-resistant (MRSA). Here, we used the 4-hydroxy-2-quinolone fragment to search the Specs database of purchasable compounds for potential inhibitors of GyrB and identified or , as a novel and potent inhibitor of the target protein (IC: 1.

Reduced expression and activity of patient-derived SHIP1 phosphatase domain mutants.

The characterization of dysregulated proteins in cell signaling pathways is important for the development of therapeutic approaches. The PI3K/AKT/mTOR pathway is frequently upregulated in cancer cells and the SH2-containing inositol 5-phosphatase SHIP1 can act as a negative regulator of the PI3K/AKT pathway. In this study, we investigated different patient-derived mutations within the conserved phosphatase domain of SHIP1.

Identification of the Natural Steroid Sapogenin Diosgenin as a Direct Dual-Specific RORα/γ Inverse Agonist.

The steroid sapogenin diosgenin is a well-known natural product with a plethora of described pharmacological activities including the amelioration of T helper 17 (Th17)-driven pathologies. However, the exact underlying mode of action of diosgenin leading to a dampened Th17 response is still largely unknown and specific molecular targets have yet to be identified. Here, we show that diosgenin acts as a direct ligand and inverse agonist of the nuclear receptor retinoic acid receptor (RAR)-related orphan receptor (ROR)α and RORγ, which are key transcription factors involved in Th17 cell differentiation and metabolism.

In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature.

The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site.

Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches.

Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach.

Ring systems in natural products: structural diversity, physicochemical properties, and coverage by synthetic compounds.

Covering: up to 2021The structural core of most small-molecule drugs is formed by a ring system, often derived from natural products. However, despite the importance of natural product ring systems in bioactive small molecules, there is still a lack of a comprehensive overview and understanding of natural product ring systems and how their full potential can be harnessed in drug discovery and related fields. Herein, we present a comprehensive cheminformatic analysis of the structural and physicochemical properties of 38 662 natural product ring systems, and the coverage of natural product ring systems by readily purchasable, synthetic compounds that are commonly explored in virtual screening and high-throughput screening.

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.

Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimate the confidence of the predictions. CP models present the advantage of ensuring a predefined error rate under the assumption that test and calibration set are exchangeable.

Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR.

The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket.