Cytotoxic, Antibacterial, and in Silico Pks13 Inhibitory Properties of Indole-Coupled Dihydrobenzo[c]phenanthridines.

The indoles and benzo[c]phenanthridines have attracted much interest as potential anticancer and antibacterial agents. Herein, the synthesis and bioactivity of new and known indole-coupled dihydrobenzo[c]phenanthridines are reported. Among the investigated compounds, 2j displays potent and selective activity against drug-resistant Staphylococcus aureus, S.

Site-of-Metabolism Prediction with Aleatoric and Epistemic Uncertainty Quantification.

In silico metabolism prediction models have become indispensable tools to optimize the metabolic properties of xenobiotics while preserving their intended biological activity. Among these, site-of-metabolism (SOM) prediction models are particularly valuable for pinpointing metabolically labile atomic positions. However, the practical utility of these models depends not only on their ability to deliver accurate predictions but also on their capacity to provide reliable estimates of predictive uncertainty.

Identification of New Lupane-Type Triterpenoids as Inverse Agonists of RAR-Related Orphan Receptor Gamma (RORγ).

Targeting retinoic acid-related orphan receptor γ (RORγ) with inverse agonists presents a promising therapeutic strategy for treating autoimmune diseases, including psoriasis, rheumatoid arthritis, and multiple sclerosis. Through structure-based virtual screening, we identified a lupane-type pentacyclic triterpenoid, (2Z)-2-(2-furanylmethylene)-3-oxolup-20(29)-en-28-oic acid (), as a new inverse agonist of RORγ. The compound exhibited IC values of 0.

Automated Annotation of Sites of Metabolism from Biotransformation Data.

Computational models predicting the Sites-of-Metabolism (SOMs) of small organic molecules have become invaluable tools for studying and optimizing the metabolic properties of xenobiotics. However, the performance of SOM predictors has shown signs of plateauing in recent years, primarily due to the limited availability of training data. While vast amounts of biotransformation data in the form of substrate-metabolite pairs exist, their potential for SOM prediction remains largely untapped due to the absence of annotations.

Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening.

Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, the emergence of machine learning (ML)-based docking approaches has opened new possibilities for enhancing VS technologies. In this study, we explore the integration of DiffDock-L, a leading ML-based pose sampling method, into VS workflows by combining it with the Vina, Gnina, and RTMScore scoring functions.

E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays.

Assay interference caused by small organic compounds continues to pose formidable challenges to early drug discovery. Various computational methods have been developed to identify compounds likely to cause assay interference. However, due to the scarcity of data available for model development, the predictive accuracy and applicability of these approaches are limited.

Integrated Virtual Screening Approach Identifies New CYP19A1 Inhibitors.

The human cytochrome P450 19A1 (CYP19A1, aromatase) is a heme-containing protein catalyzing the final steps of the biosynthesis of the steroid hormone 17β-estradiol. It is a key target for the treatment of sex-hormone-related disorders due to its role in mediating the conversion of androgens to estrogens. Here, we report the development of a virtual screening workflow incorporating machine learning and structure-based modeling that has led to the discovery of new CYP19A1 inhibitors.

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.

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.