AIMC Topic: Protein Kinase Inhibitors

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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...

Integrating machine learning with in silico studies and Quantum Chemistry: Exploring novel compounds through multiscale screening targeting the CDK2 enzyme.

Computers in biology and medicine
Cyclin-dependent kinase 2 (CDK2) modulates the progression of the cell cycle, and its dysregulation results in unchecked cellular proliferation, establishing it as a pivotal target in oncological therapies. We implemented a comprehensive screening pi...

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...

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 ...