Targeting Gatekeeper Mutations for Kinase Drug Discovery

Zhou, Yang; Xiang, Shuang; Yang, Fang; Lu, Xiaoyun

doi:10.1021/acs.jmedchem.2c01361

Cited by 28 publications

(25 citation statements)

References 121 publications

(215 reference statements)

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“…Therefore, the development of antitumor drugs targeting protein kinases has admiring all-encompassing attention. Up to now, a total of more than 70 kinase inhibitors have been launched for various diseases such as cancer, neurological disorders, inflammation, and metabolism, rendering kinase to become a major class of drug targets . Here, three launched and one preclinical kinase molecule that do not belong to USPTO-50K were used to demonstrate the utility and good generalization of our approach in drug discovery.…”

Section: Resultsmentioning

confidence: 99%

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts

Yan,

Zhao,

Yao

et al. 2023

J. Chem. Inf. Model.

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Retrosynthesis prediction is crucial in organic synthesis and drug discovery, aiding chemists in designing efficient synthetic routes for target molecules. Data-driven deep retrosynthesis prediction has gained importance due to new algorithms and enhanced computing power. Although existing models show certain predictive power on the USPTO-50K benchmark data set, no one considers the effects of byproducts during the prediction process, which may be due to the lack of byproduct information in the benchmark data set. Here, we propose a novel two-stage retrosynthesis reaction prediction framework based on byproducts called RPBP. First, RPBP predicts the byproduct involved in the reaction based on the product molecule. Then, it handles an end-to-end prediction problem based on the prediction of reactants by product and byproduct. Unlike other methods that first identify the potential reaction center and then predict reactant molecules, RPBP considers additional information from byproducts, such as reaction reagents, conditions, and sites. Interestingly, adding byproducts reduces model learning complexity in natural language processing (NLP). Our RPBP model achieves 54.7% and 66.6% top-1 retrosynthesis prediction accuracy when the reaction class is unknown and known, respectively. It outperforms existing methods for known-class reactions, thanks to the rich chemical information in byproducts. The prediction of four kinase drugs from the literature demonstrates the model’s practicality and potential to accelerate drug discovery.

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Section: Resultsmentioning

confidence: 99%

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts

Yan,

Zhao,

Yao

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…In addition, it would be an even more difficult task to experimentally validate all 1186 potentially active peptides due to the time-consuming and costly nature of chemical synthesis and bioactivity assays experiments. However, the studies of traditional discovery of biologically active molecules have sufficiently demonstrated that mutating a small portion of the fixed backbone can generates new active molecules. , Therefore, it is often crucial to consider sequence similarity in the discovery of target active peptides. Here, we only selected similar sequences to the unique potential inhibitor 17C-L20 obtained during the data set construction process for affinity determination, as peptides with high sequence similarity were more likely to had similar properties and small variations at the amino acid level could result in significant changes in activity .…”

Section: Resultsmentioning

confidence: 99%

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

Wu,

Zhu

et al. 2023

J. Chem. Inf. Model.

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“…Gatekeeper mutations represent the most common acquired "on-target" mutations responsible for inducing resistance. 128 Specifically, RET V804M/L gatekeeper mutations serve as a major resistance mechanism for first-generation MKIs. These mutations can manifest as germline mutations in sMTC and in approximately 2% of MEN2 cases.…”

Section: Limitations Of Approved Mkis While These Mkis Can Produce Be...mentioning

confidence: 99%

Unraveling the Promise of RET Inhibitors in Precision Cancer Therapy by Targeting RET Mutations

Wang,

Li,

Cai

et al. 2024

J. Med. Chem.

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Over the past decades, the role of rearranged during transfection (RET) alterations in tumorigenesis has been firmly established. RET kinase inhibition is an essential therapeutic target in patients with RET-altered cancers. In clinical practice, initial efficacy can be achieved in patients through the utilization of multikinase inhibitors (MKIs) with RET inhibitory activity. However, the effectiveness of these MKIs is impeded by the adverse events associated with off-target effects. Recently, many RET-selective inhibitors, characterized by heightened specificity and potency, have been developed, representing a substantial breakthrough in the field of RET precision oncology. This Perspective focuses on the contemporary understanding of RET mutations, recent advancements in next-generation RET inhibitors, and the challenges associated with resistance to RET inhibitors. It provides valuable insights for the development of next-generation MKIs and selective RET inhibitors.

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Targeting Gatekeeper Mutations for Kinase Drug Discovery

Cited by 28 publications

References 121 publications

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts

Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides

Unraveling the Promise of RET Inhibitors in Precision Cancer Therapy by Targeting RET Mutations

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