Ferroptosis is an iron-dependent form of oxidative cell death, and the inhibition of ferroptosis is a promising strategy with which to prevent and treat neurological diseases. Herein we report a new ferroptosis inhibitor 9a with a novel mechanism of action. It is demonstrated that nuclear receptor coactivator 4 (NCOA4), a cargo receptor for ferritinophagy, is the target of 9a. Compound 9a blocks ferroptosis by reducing the amount of bioavailable intracellular ferrous iron through disrupting the NCOA4–FTH1 protein–protein interaction. Further studies indicate that 9a directly binds to recombinant protein NCOA4^383–522 and effectively blocks the NCOA4^383–522–FTH1 interaction. In a rat model of ischemic stroke, 9a significantly ameliorates the ischemic-refusion injury. With the first ligand 9a, this work reveals that NCOA4 is a promising drug target. Additionally, 9a is the first NCOA4–FTH1 interaction inhibitor. This work paves a new road to the development of ferroptosis inhibitors against neurological diseases.

Linking fragments to generate a focused compound library for a specific drug target is one of the challenges in fragment-based drug design (FBDD). Hereby, we propose a new program named SyntaLinker, which is based on a syntactic pattern recognition approach using deep conditional transformer neural networks. This state-of-the-art transformer can link molecular fragments automatically by learning from the knowledge of structures in medicinal chemistry databases (e.g. ChEMBL database). Conventionally, linking molecular fragments was viewed as connecting substructures that were predefined by empirical rules. In SyntaLinker, however, the rules of linking fragments can be learned implicitly from known chemical structures by recognizing syntactic patterns embedded in SMILES notations. With deep conditional transformer neural networks, SyntaLinker can generate molecular structures based on a given pair of fragments and additional restrictions. Case studies have demonstrated the advantages and usefulness of SyntaLinker in FBDD.

Recognizing substructures and their relations embedded in a molecular structure representation is a key process for structure-activity or structure-property relationship (SAR/SPR) studies. A molecular structure can be either explicitly represented as a connection table (CT) or linear notation, such as SMILES, which is a language describing the connectivity of atoms in the molecular structure. Conventional SAR/SPR approaches rely on partitioning the CT into a set of predefined substructures as structural descriptors. In this work, we propose a new method to identifying SAR/SPR through linear notation (for example, SMILES) syntax analysis with self-attention mechanism, an interpretable deep learning architecture. The method has been evaluated by predicting chemical property, toxicology, and bioactivity from experimental data sets. Our results demonstrate that the method yields superior performance comparing with state-of-the-art models. Moreover, the method can produce chemically interpretable results, which can be used for a chemist to design, and synthesize the activity/property improved compounds.

Covering: up to 2010 After new human transmissible H1N1 (swine flu) viruses were reported in Mexico and the United States in April 2009, the World Health Organization (WHO) announced the emergence of a novel influenza A virus. Most governments in the world have been alerted and are monitoring the situation closely. As one of the official responses to the H1N1 pandemic, the Chinese government has released three editions of a document entitled “Recommended Schemes for Pandemic Influenza A Diagnoses and Treatments”. The third edition recommended the use of not only two targeted anti-flu drugs, oseltamivir and zanamivir, but also four anti-flu TCM (Traditional Chinese Medicine) prescriptions. Since then, TCM has played a significant role in fighting the pandemic. TCM drugs comprise multiple compounds regulating multiple targets for a given class of medical indications, and are tunable to the symptoms of the individual. This review summarizes anti-influenza agents from TCM, including compounds, herbs, and TCM prescriptions, and suggests that, by further investigating TCM theory and mining TCM databases, a better drug discovery paradigm may arise – one that can be beneficial to both TCM and modern medicine.