The recent pneumonia outbreak caused by a novel coronavirus (SARS-CoV-2) is posing a great threat to global public health. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people’s lives and preventing epidemics. It is important to establish a quick standard diagnostic test for the detection of the infectious disease (COVID-19) to prevent subsequent secondary spread. Polymerase chain reaction (PCR) is regarded as a gold standard test for the molecular diagnosis of viral and bacterial infections with high sensitivity and specificity. Isothermal nucleic acid amplification is considered to be a highly promising candidate method due to its fundamental advantage in quick procedure time at constant temperature without thermocycler operation. A variety of improved or new approaches also have been developed. This review summarizes the currently available detection methods for coronavirus nucleic acid. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coronavirus infection.

Platinum-based anticancer drugs, including cisplatin, carboplatin, oxaliplatin, nedaplatin, and lobaplatin, are heavily applied in chemotherapy regimens. However, the intrinsic or acquired resistance severely limit the clinical application of platinum-based treatment. The underlying mechanisms are incredibly complicated. Multiple transporters participate in the active transport of platinum-based antitumor agents, and the altered expression level, localization, or activity may severely decrease the cellular platinum accumulation. Detoxification components, which are commonly increasing in resistant tumor cells, can efficiently bind to platinum agents and prevent the formation of platinum–DNA adducts, but the adducts production is the determinant step for the cytotoxicity of platinum-based antitumor agents. Even if adequate adducts have formed, tumor cells still manage to survive through increased DNA repair processes or elevated apoptosis threshold. In addition, autophagy has a profound influence on platinum resistance. This review summarizes the critical participators of platinum resistance mechanisms mentioned above and highlights the most potential therapeutic targets or predicted markers. With a deeper understanding of the underlying resistance mechanisms, new solutions would be produced to extend the clinical application of platinum-based antitumor agents largely.

Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.

Graph neural networks (GNNs) have been widely used in molecular property prediction, but explaining their black-box predictions is still a challenge. Most existing explanation methods for GNNs in chemistry focus on attributing model predictions to individual nodes, edges or fragments that are not necessarily derived from a chemically meaningful segmentation of molecules. To address this challenge, we propose a method named substructure mask explanation (SME). SME is based on well-established molecular segmentation methods and provides an interpretation that aligns with the understanding of chemists. We apply SME to elucidate how GNNs learn to predict aqueous solubility, genotoxicity, cardiotoxicity and blood–brain barrier permeation for small molecules. SME provides interpretation that is consistent with the understanding of chemists, alerts them to unreliable performance, and guides them in structural optimization for target properties. Hence, we believe that SME empowers chemists to confidently mine structure-activity relationship (SAR) from reliable GNNs through a transparent inspection on how GNNs pick up useful signals when learning from data.