AIMC Topic: Protein Kinase Inhibitors

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Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer.

Oncogene Feb 26, 2025
CDK4/6 inhibitors in combination with endocrine therapy are widely used to treat HR+/HER2- metastatic breast cancer leading to improved progression-free survival (PFS) compared to single agent endocrine therapy. Over 300 patients receiving standard-o...
Humans Gene Expression Regulation, Neoplastic Progression-Free Survival Neoplasm Metastasis Protein Kinase Inhibitors Cyclin-Dependent Kinase 4 Cyclin-Dependent Kinase 6 Machine Learning Prognosis Female Aged Adult Breast Neoplasms Middle Aged
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A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

The Journal of pathology Feb 14, 2025
Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predict response to tyrosine kinase inhibitor (TKI) therapies, as recommended by NCCN (National Comprehensive Cancer Network) guidelines. However, gene seq...
Treatment Outcome Mutation Gastrointestinal Neoplasms Gastrointestinal Stromal Tumors Aged Male Middle Aged Deep Learning Imatinib Mesylate Pyrazoles Protein Kinase Inhibitors Pyrroles Triazines Humans Proto-Oncogene Proteins c-kit Female Tyrosine Kinase Inhibitors Receptor, Platelet-Derived Growth Factor alpha
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

BMC gastroenterology Jan 21, 2025
OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarteri...
Protein Kinase Inhibitors Tyrosine Kinase Inhibitors Antineoplastic Combined Chemotherapy Protocols Combined Modality Therapy Chemoembolization, Therapeutic Adult Radiomics Male Middle Aged Deep Learning Liver Neoplasms Carcinoma, Hepatocellular Immune Checkpoint Inhibitors Retrospective Studies Humans Tomography, X-Ray Computed Prognosis Female Aged
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Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.

Scientific reports Jan 13, 2025
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivit...
Humans Machine Learning Molecular Dynamics Simulation Protein Kinase Inhibitors src-Family Kinases Molecular Docking Simulation Inflammation Support Vector Machine Drug Repositioning
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Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling Dec 17, 2024
Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges d...
Adenosine Triphosphate Ligands Protein Kinase Inhibitors Binding Sites Molecular Docking Simulation Drug Discovery Deep Learning Humans
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Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling Dec 10, 2024
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...
Humans Machine Learning Molecular Docking Simulation Protein Binding Protein Kinases Protein Conformation Protein Kinase Inhibitors Binding Sites
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS Dec 5, 2024
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...
Oleanolic Acid Signal Transduction Mutation Carcinoma, Non-Small-Cell Lung Mice, Nude Humans Xenograft Model Antitumor Assays Cell Line, Tumor Apoptosis Machine Learning ErbB Receptors Lung Neoplasms Animals Protein Kinase Inhibitors Mice Antineoplastic Agents Cell Proliferation
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Using bioinformatics and artificial intelligence to map the cyclin-dependent kinase 4/6 inhibitor biomarker landscape in breast cancer.

Future oncology (London, England) Nov 12, 2024
A cyclin-dependent kinase 4/6 (CDK4/6) inhibitor combined with endocrine therapy is the standard-of-care for patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer. However, not all patients r...
Humans Artificial Intelligence Female Protein Kinase Inhibitors Breast Neoplasms Cyclin-Dependent Kinase 4 Biomarkers, Tumor Cyclin-Dependent Kinase 6 Computational Biology Drug Resistance, Neoplasm
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Wee1 inhibitor optimization through deep-learning-driven decision making.

European journal of medicinal chemistry Sep 29, 2024
Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, w...
Decision Making Protein Kinase Inhibitors Humans Cell Line, Tumor Drug Screening Assays, Antitumor Cell Cycle Proteins Antineoplastic Agents Cell Proliferation Structure-Activity Relationship Deep Learning Dose-Response Relationship, Drug Molecular Structure Protein-Tyrosine Kinases
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The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform.

Journal of chemical information and modeling Sep 25, 2024
Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmaco...
Small Molecule Libraries Humans Deep Learning Drug Discovery Protein Kinases Protein Kinase Inhibitors
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