Design, synthesis, and biological evaluation of 5-chlorine-2-amino-pyrimidine derivatives as potent PLK4 inhibitors.

Serine/threonine kinase PLK4, a critical regulator of centrosome duplication, is closely associated with tumorigenesis due to its role in centrosome amplification. PLK4 is overexpressed in multiple cancers and has recently emerged as a promising therapeutic target for TRIM37-amplified breast cancer. Developing safe and effective PLK4 inhibitors holds significant therapeutic potential.

Rational design of CZL-S092: A novel indazole-based PLK4 inhibitor targeting neuroblastoma through virtual screening and fragment-based drug design strategies.

Polo like kinase 4 (PLK4) is a critical member of the polo-like kinase family that works as a master regulator of centriole duplication and mitotic progression. Overexpression of PLK4 has been documented in various cancers and PLK4 inhibitors are regarded as a potential strategy for cancer treatment. In this study, we identified indazole derivative 8 as a hit compound through a virtual screening approach.

Design, synthesis, and biological evaluation of novel -(1-indazol-6-yl)benzenesulfonamide derivatives as potent PLK4 inhibitors.

PLK4 is a serine/threonine protein kinase situated at the centrosome, acting as a crucial regulatory element in the regulation of cell mitosis and significantly contributing to the preservation of genomic integrity. The overexpression of PLK4 is intricately linked to the onset and progression of several cancers, influencing a range of actions in tumor cells, such as proliferation, differentiation, migration, and invasion. PLK4 has been identified as a target for the therapy of several malignancies, especially breast cancer characterized by elevated levels.

Robust convex biclustering with a tuning-free method.

Biclustering is widely used in different kinds of fields including gene information analysis, text mining, and recommendation system by effectively discovering the local correlation between samples and features. However, many biclustering algorithms will collapse when facing heavy-tailed data. In this paper, we propose a robust version of convex biclustering algorithm with Huber loss.