AIMC Topic: Protein Kinase Inhibitors

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Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.

Molecular diversity Jan 22, 2025
The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug com...
Piperidines Molecular Targeted Therapy Protein Kinase Inhibitors Indazoles Poly(ADP-ribose) Polymerase Inhibitors Lapatinib Blood-Brain Barrier Brain Neoplasms Molecular Docking Simulation Molecular Dynamics Simulation Humans Machine Learning Computer Simulation
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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 Radiomics Male Middle Aged Deep Learning Liver Neoplasms Carcinoma, Hepatocellular Immune Checkpoint Inhibitors Tomography, X-Ray Computed Female Aged Adult Humans Prognosis Retrospective Studies
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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 Drug Repositioning Molecular Dynamics Simulation Protein Kinase Inhibitors src-Family Kinases Molecular Docking Simulation Inflammation Support Vector Machine
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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...
Ligands Molecular Docking Simulation Drug Discovery Protein Kinase Inhibitors Humans Deep Learning Binding Sites Adenosine Triphosphate
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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...
Protein Kinases Protein Conformation Protein Kinase Inhibitors Binding Sites Humans Machine Learning Molecular Docking Simulation Protein Binding
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iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.

Molecular diversity Dec 8, 2024
Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promisi...
Deep Learning Triple Negative Breast Neoplasms Phosphatidylinositol 3-Kinases Protein Kinase Inhibitors Female Molecular Docking Simulation Antineoplastic Agents Phosphoinositide-3 Kinase Inhibitors Humans
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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...
Humans Machine Learning Animals Mice, Nude Apoptosis Mutation Xenograft Model Antitumor Assays ErbB Receptors Mice Antineoplastic Agents Protein Kinase Inhibitors Cell Proliferation Oleanolic Acid Cell Line, Tumor Signal Transduction Lung Neoplasms Carcinoma, Non-Small-Cell Lung
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Classification models and SAR analysis of anaplastic lymphoma kinase (ALK) inhibitors using machine learning algorithms with two data division methods.

Molecular diversity Nov 12, 2024
Anaplastic lymphoma kinase (ALK) plays a critical role in the development of various cancers. In this study, the dataset of 1810 collected inhibitors were divided into a training set and a test set by the self-organizing map (SOM) and random method, ...
Support Vector Machine Structure-Activity Relationship Protein Kinase Inhibitors Anaplastic Lymphoma Kinase Humans Machine Learning Algorithms
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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...
Biomarkers, Tumor Computational Biology Cyclin-Dependent Kinase 4 Humans Drug Resistance, Neoplasm Cyclin-Dependent Kinase 6 Artificial Intelligence Protein Kinase Inhibitors Female Breast Neoplasms
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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 Cell Line, Tumor Humans Drug Screening Assays, Antitumor Molecular Structure Cell Proliferation Structure-Activity Relationship Antineoplastic Agents Protein Kinase Inhibitors Dose-Response Relationship, Drug Deep Learning Cell Cycle Proteins Protein-Tyrosine Kinases
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