Protein Kinase Inhibitors

Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling 39690486

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 Binding Sites Deep Learning Drug Discovery Humans Ligands Molecular Docking Simulation Protein Kinase Inhibitors

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Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling 39657274

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...

Binding Sites Humans Machine Learning Molecular Docking Simulation Protein Binding Protein Conformation Protein Kinase Inhibitors Protein Kinases

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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 39639305

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...

Animals Antineoplastic Agents Apoptosis Carcinoma, Non-Small-Cell Lung Cell Line, Tumor Cell Proliferation ErbB Receptors Humans Lung Neoplasms Machine Learning Mice Mice, Nude Mutation Oleanolic Acid Protein Kinase Inhibitors Signal Transduction Xenograft Model Antitumor Assays

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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 39838292

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...

Adult Aged Antineoplastic Combined Chemotherapy Protocols Carcinoma, Hepatocellular Chemoembolization, Therapeutic Combined Modality Therapy Deep Learning Female Humans Immune Checkpoint Inhibitors Liver Neoplasms Male Middle Aged Prognosis Protein Kinase Inhibitors Radiomics Retrospective Studies Tomography, X-Ray Computed Tyrosine Kinase Inhibitors

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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 39805859

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...

Drug Repositioning Humans Inflammation Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Protein Kinase Inhibitors src-Family Kinases Support Vector Machine

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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 40011574

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...

Adult Aged Breast Neoplasms Cyclin-Dependent Kinase 4 Cyclin-Dependent Kinase 6 Female Gene Expression Regulation, Neoplastic Humans Machine Learning Middle Aged Neoplasm Metastasis Prognosis Progression-Free Survival Protein Kinase Inhibitors

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Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

Molecular informatics 39976334

The human epidermal growth factor receptor 2 (HER2) is a critical oncogene implicated in the development of various aggressive cancers, particularly breast cancer. Discovering novel HER2 inhibitors is crucial for expanding therapeutic options for HER...

Antineoplastic Agents Drug Discovery Humans Machine Learning Molecular Docking Simulation Pharmacophore Protein Kinase Inhibitors Quantitative Structure-Activity Relationship Receptor, ErbB-2

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A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

The Journal of pathology 39950223

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...

Aged Deep Learning Female Gastrointestinal Neoplasms Gastrointestinal Stromal Tumors Humans Imatinib Mesylate Male Middle Aged Mutation Protein Kinase Inhibitors Proto-Oncogene Proteins c-kit Pyrazoles Pyrroles Receptor, Platelet-Derived Growth Factor alpha Treatment Outcome Triazines Tyrosine Kinase Inhibitors

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Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.

The journal of physical chemistry. B 40053865

Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inh...

Allosteric Regulation ErbB Receptors Humans Machine Learning Molecular Dynamics Simulation Mutation Protein Kinase Inhibitors Quantitative Structure-Activity Relationship

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Increase Docking Score Screening Power by Simple Fusion With CNNscore.

Journal of computational chemistry 39981784

Scoring functions (SFs) of molecular docking is a vital component of structure-based virtual screening (SBVS). Traditional SFs yield their inherent shortage for idealized approximations and simplifications predicting the binding affinity. Complementa...

Deep Learning Drug Evaluation, Preclinical Humans Ligands Molecular Docking Simulation Neural Networks, Computer Protein Kinase Inhibitors

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Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.

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

Discovery of New HER2 Inhibitors via Computational Docking, Pharmacophore Modeling, and Machine Learning.

A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.

Increase Docking Score Screening Power by Simple Fusion With CNNscore.

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