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Ischemia-reperfusion injury (IRI) is a major cause of cardiac damage following various pathological processes, such as free radical damage and cell apoptosis. This study aims to investigate whether microRNA-292-5p (miR-292-5p) protects against myocardial ischemia-reperfusion injury (IRI) via the peroxisome proliferator-activated receptor (PPAR)-α/-γ signaling pathway in myocardial IRI mice models. Mouse models of myocardial IRI were established. Adult male C57BL/6 mice were divided into different groups. The hemodynamic indexes, levels of related inflammatory factors and serum myocardial enzymes, and malondialdehyde (MDA) content and the activity of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were detected. The 2,3,5-triphenyltetrazolium chloride (TTC) staining was applied to determine infarct size. TUNEL staining was used to detect cardiomyocyte apoptosis. RT-qPCR and western blotting were performed to measure the related gene expressions. Compared with the model group and the T0070907 + miR-292-5p inhibitor, the miR-292-5p inhibitor group exhibited decreased incidence and duration time of ventricular tachycardia and ventricular fibrillation, serum myocardial enzymes, TNF-α, IL-6, IL-1β, MDA, cardiomyocyte apoptosis, expressions of Bax and p53 in addition to increased SOD and GSH-Px activity, and increased expressions of Bcl-2, PPARα, PPARγ, PLIN5, AQP7, and PCK1. The T0070907 group exhibited opposite results compared to the miR-292-5p inhibitor group. The results indicate that miR-292-5p downregulation protects against myocardial IRI through activation of the PPAR-α/PPAR-γ signaling pathway.

Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.

We present a Focused Library Generator that is able to create from scratch new molecules with desired properties. After training the Generator on the ChEMBL database, transfer learning was used to switch the generator to producing new Mdmx inhibitors that are a promising class of anticancer drugs. Lilly medicinal chemistry filters, molecular docking, and a QSAR IC 50 model were used to refine the output of the Generator. Pharmacophore screening and molecular dynamics (MD) simulations were then used to further select putative ligands. Finally, we identified five promising hits with equivalent or even better predicted binding free energies and IC 50 values than known Mdmx inhibitors. The

Human microsomal prostaglandin [Formula: see text] synthase (mPGES)-1 is a promising drug target for inflammation and other diseases with inflammatory symptoms. In this work, we built classification models which were able to classify mPGES-1 inhibitors into two groups: highly active inhibitors and weakly active inhibitors. A dataset of 1910 mPGES-1 inhibitors was separated into a training set and a test set by two methods, by a Kohonen's self-organizing map or by random selection. The molecules were represented by different types of fingerprint descriptors including MACCS keys (MACCS), CDK fingerprints, Estate fingerprints, PubChem fingerprints, substructure fingerprints and 2D atom pairs fingerprint. First, we used a support vector machine (SVM) to build twelve models with six types of fingerprints and found that MACCS had some advantage over the other fingerprints in modeling. Next, we used naïve Bayes (NB), random forest (RF) and multilayer perceptron (MLP) methods to build six models with MACCS only and found that models using RF and MLP methods were better than NB. Finally, all the models with MACCS keys were used to make predictions on an external test set of 41 compounds. In summary, the models built with MACCS keys and using SVM, RF and MLP methods show good prediction performance on the test sets and the external test set. Furthermore, we made a structure-activity relationship analysis between mPGES-1 and its inhibitors based on the information gain of fingerprints and could pinpoint some key functional groups for mPGES-1 activity. It was found that highly active inhibitors usually contained an amide group, an aromatic ring or a nitrogen heterocyclic ring, and several heteroatoms substituents such as fluorine and chlorine. The carboxyl group and sulfur atom groups mainly appeared in weakly active inhibitors.

Alkynes are widely used in chemistry, medicine and materials science. Here we demonstrate a transition-metal and photocatalyst-free inverse Sonogashira coupling reaction between iodoalkynes and (hetero)arenes or alkenes under visible-light irradiation....

Online Chemical Modeling Environment (OCHEM) was used for QSAR analysis of a set of ionic liquids (ILs) tested against multi-drug resistant (MDR) clinical isolate Acinetobacter baumannii and Staphylococcus aureus strains. The predictive accuracy of regression models has coefficient of determination q2 = 0.66 − 0.79 with cross-validation and independent test sets. The models were used to screen a virtual chemical library of ILs, which was designed with targeted activity against MDR Acinetobacter baumannii and Staphylococcus aureus strains. Seven most promising ILs were selected, synthesized, and tested. Three ILs showed high activity against both these MDR clinical isolates.