AIMC Topic: Protein Kinase Inhibitors

Clear Filters Showing 1 to 10 of 149 articles

In silico analysis of atrial fibrillation and hypertension mechanism of action secondary to ibrutinib/acalabrutinib in chronic lymphocytic leukemia.

Scientific reports
Ibrutinib and acalabrutinib are first- and next-generation Bruton Tyrosine Kinase inhibitors (BTKi), respectively, approved for chronic lymphocytic leukemia (CLL). Ibrutinib has been associated with cardiovascular events, including atrial fibrillatio...

Sequence-based virtual screening using transformers.

Nature communications
Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identificatio...

Integrated machine learning and deep learning-based virtual screening framework identifies novel natural GSK-3β inhibitors for Alzheimer's disease.

Journal of computer-aided molecular design
Alzheimer's disease (AD) is a progressive neurodegenerative disorder lacking effective therapies. Glycogen synthase kinase-3β (GSK-3β), a key regulator of Aβ aggregation and Tau hyperphosphorylation, has emerged as a promising therapeutic target. Her...

Machine Learning-Assisted Iterative Screening for Efficient Detection of Drug Discovery Starting Points.

Journal of medicinal chemistry
High-throughput screening (HTS) remains central to small molecule lead discovery, but increasing assay complexity challenges the screening of large compound libraries. While retrospective studies have assessed active-learning-guided screening, extens...

A computational study of cardiac glycosides from Vernonia amygdalina as PI3K inhibitors for targeting HER2 positive breast cancer.

Journal of computer-aided molecular design
The PI3K/Akt pathway plays a crucial role in regulating a broad network of proteins involved in the proliferation of HER2-positive breast cancer. The ethyl acetate fraction of Vernonia amygdalina, which contains cardiac glycosides, has been shown to ...

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry.

Scientific reports
Early diagnosis and treatment initiation of chronic myeloid leukemia (CML) are considered to increase the rate of deep molecular response. However, the early diagnosis of CML is challenging due to the absence of clinical symptoms and peripheral blood...

Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy.

Scientific reports
Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are a growing global health concern, especially among the elderly, posing significant challenges to well-being and survival. GSK3β, a serine/threonine...

Histopathologic deep learning model for predicting tumor response to hepatic arterial infusion chemotherapy plus TKIs and ICIs in large hepatocellular carcinoma.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: While triplet therapy (HTI), which combines hepatic arterial infusion chemotherapy (HAIC) with tyrosine kinase inhibitors and immune checkpoint inhibitors, is widely used in the treatment of large hepatocellular carcinoma (HCC), there are...