Design, Synthesis, and Biological Evaluation of Dual c-Met/HDAC Inhibitors Bearing 2-Aminopyrimidine Scaffold

Zhang, Qingwei; Xu, Guili; Bao, Ya; Jiao, Min-Ru; Li, Jianqi

doi:10.1055/s-0040-1722543

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…However, there are certain requirements for the spatial location of the ATP binding pockets and the substrate, and new skeletal structures need to be developed, while few studies have been conducted. As JNK-specific inhibitors have achieved limited success, multitarget inhibitors may be a novel approach for inhibitor development . For example, Compounds 38 and 39 have simultaneously targeted both JNK3 and p38α kinases, resulting in dual inhibitors with nanomolar activity .…”

Section: Summary and Outlookmentioning

confidence: 99%

Unraveling the Design and Discovery of c-Jun N-Terminal Kinase Inhibitors and Their Therapeutic Potential in Human Diseases

et al. 2022

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c-Jun N-terminal kinases (JNKs), members of the mitogen-activated protein kinase (MAPK) family, are encoded by three genes: jnk1, jnk2, and jnk3. JNKs are involved in the pathogenesis and development of many diseases, such as neurodegenerative diseases, inflammation, and cancers. Therefore, JNKs have become important therapeutic targets. Many JNK inhibitors have been discovered, and some have been introduced into clinical trials. However, the study of isoform-selective JNK inhibitors is still a challenging task. To further develop novel JNK inhibitors with clinical value, a comprehensive understanding of JNKs and their corresponding inhibitors is required. In this Perspective, we introduced the JNK signaling pathways and reviewed different chemical types of JNK inhibitors, focusing on their structure–activity relationships and biological activities. The challenges and strategies for the development of JNK inhibitors are also discussed. It is hoped that this Perspective will provide valuable references for the development of novel selective JNK inhibitors.

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Section: Summary and Outlookmentioning

confidence: 99%

Unraveling the Design and Discovery of c-Jun N-Terminal Kinase Inhibitors and Their Therapeutic Potential in Human Diseases

et al. 2022

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Exploring the landscape of post-translational modification in drug discovery

Cao,

Yu,

Zhu

et al. 2025

Pharmacology & Therapeutics

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An enhanced cascade-based deep forest model for drug combination prediction

Lin

Zhang

et al. 2022

Briefings in Bioinformatics

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Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

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Design, Synthesis, and Biological Evaluation of Dual c-Met/HDAC Inhibitors Bearing 2-Aminopyrimidine Scaffold

Cited by 3 publications

References 16 publications

Unraveling the Design and Discovery of c-Jun N-Terminal Kinase Inhibitors and Their Therapeutic Potential in Human Diseases

Unraveling the Design and Discovery of c-Jun N-Terminal Kinase Inhibitors and Their Therapeutic Potential in Human Diseases

Exploring the landscape of post-translational modification in drug discovery

An enhanced cascade-based deep forest model for drug combination prediction

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