AIMC Topic: Agammaglobulinaemia Tyrosine Kinase

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Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments.

Journal of chemical information and modeling
Small-molecule drugs play a critical role in cancer therapy by selectively targeting key signaling pathways that drive tumor growth. While deep learning models have advanced drug discovery, there remains a lack of generative frameworks for covalent ...

Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability.

Molecular diversity
In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. T...

Bayesian machine learning to discover Bruton's tyrosine kinase inhibitors.

Chemical biology & drug design
Bruton's tyrosine kinase (BTK) has a crucial role in multiple cell signaling pathways including B-cell antigen receptor (BCR) and Fc receptor (FcR) signaling cascades, which has attracted much attention to find BTK inhibitors to treat autoimmune dise...

Protein Profiles Predict Treatment Responses to the PI3K Inhibitor Umbralisib in Patients with Chronic Lymphocytic Leukemia.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: The management of chronic lymphocytic leukemia (CLL) has significantly improved with targeted therapies. However, many patients experience a suboptimal response. To optimally select the best therapy, predictive biomarkers are necessary. In t...