2-oxazolidinone is well known as a pharmacophore for antibacterial agents represented by two marketed medicines, Linezolid and Tedizolid. On the other hand, there are growing reports on the various biological activities of 2-oxazolidinones beyond antibacterial activities. Therefore, in this review, we provide an overview of the progress of this untraditional area of 2-oxazolidinones in the past 10 years (2011-2021).

Bronchial asthma is the most common chronic respiratory illness, the incidence of which continues to increase annually. Currently, effective treatments for CS-resistant asthma and severe asthma are still lacking, and new therapeutic regimens are urgently required. PI3Kδ is a key enzyme in hematopoietic cells and represents a major target for oncology and inflammatory disease (particularly respiratory disease, asthma and COPD). In the case of respiratory disease, the ability to inhibit PI3Kδ in the lungs shows a higher safety and therapeutic index relative to systemic inhibition. In recent years, paradigm shifts have occurred in inhalation therapeutics for systemic and topical drug delivery, due to the favorable properties of lungs including their large surface area and high permeability. Pulmonary drug delivery possesses many advantages, including a non-invasive route of administration, low metabolic activity, a controlled environment for systemic absorption and the ability to avoid first bypass metabolism. In this review, we focus on the discovery and development of inhaled drugs targeting PI3Kδ for asthma, by focusing on their activity and selectivity, in addition to their potential in drug design strategies using inhaled administration.

An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new graph-attention-based approach, is proposed to improve the accuracy of molecular representation by simultaneously considering atom–atom, bond–bond and atom-bond interactions. In addition, we benchmark models extensively on 8 public and 680 proprietary industrial datasets spanning a wide variety of chemical end points. The results show that ComABAN has higher prediction accuracy compared with the classical machine learning method and the deep learning-based methods. Furthermore, the trained neural network was used to predict a library of 1.5 million molecules and picked out compounds with a classification result of grade I. Subsequently, these predicted molecules were scored and ranked using cascade docking, molecular dynamics simulations to generate five potential candidates. All five molecules showed high similarity to nanomolar bioactive inhibitors suppressing the expression of HIF-1α, and we synthesized three compounds (Y-1, Y-3, Y-4) and tested their inhibitory ability in vitro. Our results indicate that ComABAN is an effective tool for accelerating drug discovery.