Tao Lu

TBADT‐catalyzed carbamoylation under heat‐free neutral conditions has been reported. Unlike conventional reports, the addition of TBAB, which could promote the TBADT catalytic system via bromide─hydrogen bonding network was the key to success, affording structurally diverse carboxamides in improved yields. Notably, the catalytic system has high atom‐economy, low E‐Factors, that is compatible with late‐stage functionalization.

Novel alcohol oxidation via transfer hydrogenation enables efficient, eco-friendly synthesis with broad scope and concurrent pharmaceutical intermediate production.

The p53 protein is regarded as the “Guardian of the Genome,” but its mutation is tumor progression and present in more than half of malignant tumors. The pro-metastatic property of mutant p53 makes a strong argument for targeting mutant p53 with new therapeutic strategies. However, mutant p53 was considered as a challenging target for drug discovery due to the lack of small molecular binding pockets. Among them, mutant p53 Y220C creates a narrow crevice since the side chains dynamics on protein surface, which is suitable for designing small molecules to occupy the cavity and recovery the tumor suppressing function. Here, we describe the mechanism of p53 related signal pathway and how p53 Y220C regulate the tumorigenesis. We review the two types of p53 Y220C modulators including restoring the conformation of mutant p53 Y220C protein to wild-type p53 protein and recruiting histone acetyltransferase p300/CBP to acetylate p53 Y220C thus enables p53 Y220C dependent upregulation of apoptotic genes and downregulation of DNA damage response pathways. We also report clinical advances and challenges of these molecules in p53 Y220C medicated tumor therapy.

Protein–ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein–ligand binding affinity is a crucial metric of the strength of the interaction between the two, and accurate prediction of its binding affinity is essential for discovering drugs' new uses. So far, although many predictive models based on machine learning and deep learning have been reported, most of the models mainly focus on one-dimensional sequence and two-dimensional structural characteristics of proteins and ligands, but fail to deeply explore the detailed interaction information between proteins and ligand atoms in the binding pocket region of three-dimensional space. In this study, we introduced a novel 4D tensor feature to capture key interactions within the binding pocket and developed a three-dimensional convolutional neural network (CNN) model based on this feature. Using ten-fold cross-validation, we identified the optimal parameter combination and pocket size. Additionally, we employed feature engineering to extract features across multiple dimensions, including one-dimensional sequences, two-dimensional structures of the ligand and protein, and three-dimensional interaction features between them. We proposed an efficient protein–ligand binding affinity prediction model MCDTA (multi-dimensional convolutional drug–target affinity), built on a multi-dimensional convolutional neural network framework. Feature ablation experiments revealed that the 4D tensor feature had the most significant impact on model performance. MCDTA performed exceptionally well on the PDBbind v.2020 dataset, achieving an RMSE of 1.231 and a PCC of 0.823. In comparative experiments, it outperformed five other mainstream binding affinity prediction models, with an RMSE of 1.349 and a PCC of 0.795. Moreover, MCDTA demonstrated strong generalization ability and practical screening performance across multiple benchmark datasets, highlighting its reliability and accuracy in predicting protein–ligand binding affinity. The code for MCDTA is available at https://github.com/dfhuang-AI/MCDTA. Graphical Abstract