Design, synthesis and activity evaluation of 4-(quinoline-2-yl)aniline derivatives as SARS-CoV‑2 main protease inhibitors.

Since 2020, numerous compounds have been investigated for their potential use in treating SARS-CoV-2 infections. By identifying the molecular targets during the virus replication process, rationally designed anti-SARS-CoV-2 agents are developed. Among these targets, the main protease (M) is a crucial enzyme required for virus replication, and its highly conserved characteristic make it an important drug target for the development of anti-SARS-CoV-2 drugs.

Benchmark of Data Integration in Single-Cell Proteomics.

Single-cell proteomics (SCP) detected based on different technologies always involves batch-specific variations because of differences in sample processing and other potential biases. How to integrate SCP data effectively has become a great challenge. Integration of SCP data not only requires the conservation of true biological variances, but also realizes the removal of unwanted batch effects.

Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models.

KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment K ≈ 0.

Discovery of Novel Noncovalent KRAS G12D Inhibitors through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

The development of effective inhibitors targeting the Kirsten rat sarcoma viral proto-oncogene (KRAS) mutation, a prevalent oncogenic driver in cancer, represents a significant unmet need in precision medicine. In this study, an integrated computational approach combining structure-based virtual screening and molecular dynamics simulation was employed to identify novel noncovalent inhibitors targeting the KRAS variant. Through virtual screening of over 1.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics.

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics.

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset.

Classification for psychiatric disorders including schizophrenia, bipolar disorder, and major depressive disorder using machine learning.

Schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD) are the most common psychiatric disorders. Because there were lots of overlaps among these disorders from genetic epidemiology and molecular genetics, it is hard to realize the diagnoses of these psychiatric disorders. Currently, plenty of studies have been conducted for contributing to the diagnoses of these diseases.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data.

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes.

Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data.

Thyroid nodules are present in upto 50% of the population worldwide, and thyroid malignancy occurs in only 5-15% of nodules. Until now, fine-needle biopsy with cytologic evaluation remains the diagnostic choice to determine the risk of malignancy, yet it fails to discriminate as benign or malignant in one-third of cases. In order to improve the diagnostic accuracy and reliability, molecular testing based on transcriptomic data has developed rapidly.

Syntheses, properties and bio-activities of water-soluble CO-releasing molecule based on manganese.

A series of water-soluble CO-releasing molecules, [Mn(CO)3NH2CHRCO2]2 (1-3), [M(CO)3Br[(Py-C = N)(Gly) n CO2] (M = Mn, Re, 4-7), Mn(CO)4[S2CNC m H n CO2] (8-12), were synthesized and characterized by (1)H NMR, IR and ESI-HRMS. The stability of all the complexes in solution was evaluated by means of UV, IR and (1)H NMR. Among all the complexes, complex 4 and complex 8 were stable in H2O, acidic aqueous solution and basic media; complex 1 was stable in acidic aqueous solution and weak basic media (pH < 9.

Toxicity, bio-distribution and metabolism of CO-releasing molecules based on cobalt.

CO-releasing molecules (CORMs) containing [Co2(CO)6] moiety show many bioactivities, such as anti-inflammatory and antitumor cell proliferation. However, so far, no one knows their properties in vivo. So, here, we evaluated some these kind CORMs from drug-like properties including cytotoxicity, toxicity in vivo, distribution and metabolism.

[Toxicology and bioactivities of CO-releasing molecules based on cobalt complexes].

Complexes containing cobalt and carbon monoxide ligands, CO releasing molecules(CORMs), have the potential of anti-tumor and anti-inflammatory. In this paper, three hybrid CORMs 1-3 were synthesized and tested for their toxicology in vivo and bioactivities. The results suggest that the complexes have a long half-life in the range of 43-53 min; their oral LD(50) to mouse are between 1 500 mg·kg(-1) and 5 000 mg·kg(-1).