AIMC Topic: Protein Kinase Inhibitors

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kinCSM-RTK: Machine Learning-Based Screening of Receptor Tyrosine Kinase Inhibitors in Drug Discovery.

Journal of chemical information and modeling
Receptor tyrosine kinases (RTKs) are key regulators of cellular functions, such as differentiation, migration and proliferation. Dysregulated RTK activity contributes to various diseases, including neurological disorders and cancer, for which small m...

Machine learning-guided identification and simulation-based validation of potent JAK3 inhibitors for cancer therapy.

PloS one
Janus kinase 3 (JAK3) is a hematopoietic-specific kinase implicated in cytokine signaling and immune dysregulation and has recently been associated with cancer progression. However, selective and potent JAK3 inhibitors remain underdeveloped. In this ...

Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.

Nature communications
Developing selective kinase inhibitors is challenging due to the conserved kinase structures and costly kinome profiling experiments, highlighting the need for accurate prediction of kinase-inhibitor affinity and specificity. Here we present MMCLKin,...

Comparison of in vitro migration assays evaluating nintedanib's migration inhibitory effects on melanoma cells.

Scientific reports
Cell migration plays a central role in tumor progression and metastasis, making it a critical parameter in both cancer biology and therapeutic evaluation. A range of in vitro migration assays are commonly used to assess treatment-induced effects on m...

Multi-stage variational autoencoders for hierarchical molecular generation and activity optimization.

Journal of computer-aided molecular design
Deep generative models may detect novel compounds with favourable features, exhibiting chemical design potential. Traditional single-stage variational autoencoders (VAEs) lack validity, uniqueness, and biologically meaningful distribution alignment. ...

Synergistic approach utilizing bioinformatics, machine learning, and traditional screening for the identification of novel CSK inhibitors targeting hepatocellular carcinoma.

Journal of computer-aided molecular design
The overexpression or activation of C-terminal Src kinase (CSK) has been recognized as a pivotal factor in the progression of hepatocellular carcinoma (HCC), positioning CSK as a promising therapeutic target. Despite this potential, no CSK-specific i...

Drug repurposing identifies novel Wee1 kinase inhibitors for triple negative breast cancer therapeutics.

European journal of medicinal chemistry
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options. Wee1 kinase, a critical regulator of the G2/M checkpoint and DNA replication, is a promising therapeutic target. However, dose dependent as...

DeepEGFR a graph neural network for bioactivity classification of EGFR inhibitors.

Scientific reports
Epidermal Growth Factor Receptor (EGFR) plays a critical role in the development of several cancers. Thus, modulation/inhibition of EGFR activity is an appealing target of developing novel cancer therapeutics. With the advent of modern machine learni...

Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.

Journal for immunotherapy of cancer
PURPOSE: This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an ar...

Identification of novel DYRK1A inhibitors as treatment options for alzheimer's disease through comprehensive in silico approaches.

Scientific reports
This study aims to identify potential DYRK1A inhibitors from a curated database and utilize a QSAR model to predict the bioactivity of drug compounds in inhibiting the enzyme involved in tau protein oligomerization, a key process in AD pathology. 192...