AIMC Topic: ErbB Receptors

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Sequence-based virtual screening using transformers.

Nature communications Jul 28, 2025
Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identificatio...
Proto-Oncogene Proteins c-abl Deep Learning Protein Binding Drug Evaluation, Preclinical Amino Acid Sequence Ligands Protein Conformation ErbB Receptors Humans Protein Kinase Inhibitors Drug Design
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Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.

BMC pulmonary medicine Jul 10, 2025
OBJECTIVES: To investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status.
Humans Retrospective Studies Tomography, X-Ray Computed Female Deep Learning China Lung Neoplasms Carcinoma, Non-Small-Cell Lung Aged Adult Male Middle Aged ErbB Receptors Protein Kinase Inhibitors Mutation Drug Resistance, Neoplasm Tumor Suppressor Protein p53
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Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments.

Journal of chemical information and modeling Jun 5, 2025
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 ...
Protein Kinase Inhibitors Agammaglobulinaemia Tyrosine Kinase Small Molecule Libraries Humans Molecular Docking Simulation Drug Design Deep Learning Molecular Dynamics Simulation Ligands ErbB Receptors
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Exploring Hidden Dangers: Predicting Mycotoxin-like Toxicity and Mapping Toxicological Networks in Hepatocellular Carcinoma.

Journal of chemical information and modeling May 20, 2025
Mycotoxins are potent triggers of hepatocellular carcinoma (HCC) due to their intricate interplay with cellular macromolecules and signaling pathways. This study integrates machine learning and biomolecular analyses to elucidate the mechanisms underl...
Humans Molecular Docking Simulation Machine Learning Liver Neoplasms Signal Transduction Carcinoma, Hepatocellular Mycotoxins Protein Interaction Maps ErbB Receptors
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Prediction of EGFR Mutations in Lung Adenocarcinoma via CT Images: A Comparative Study of Intratumoral and Peritumoral Radiomics, Deep Learning, and Fusion Models.

Academic radiology May 5, 2025
RATIONALE AND OBJECTIVES: This study aims to analyze the intratumoral and peritumoral characteristics of lung adenocarcinoma patients on the basis of chest CT images via radiomic and deep learning methods and to develop and validate a multimodel fusi...
Tomography, X-Ray Computed Female Aged Radiomics Humans Retrospective Studies Mutation Radiographic Image Interpretation, Computer-Assisted ErbB Receptors Adenocarcinoma of Lung Male Middle Aged Deep Learning Lung Neoplasms
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Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BMC cancer May 1, 2025
BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associated with dismal prognosis. Lung cancer brain metastases (LCBM) are a common complication of lung can...
Prognosis Brain Neoplasms Lung Neoplasms Humans Carcinoma, Non-Small-Cell Lung Mutation ErbB Receptors Machine Learning
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Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Journal of thoracic imaging May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Aged Adult Retrospective Studies Sensitivity and Specificity ErbB Receptors Radiomics Tomography, X-Ray Computed Adenocarcinoma of Lung Male Lung Neoplasms Humans Female Middle Aged Mutation Reproducibility of Results Lung Tumor Suppressor Protein p53 Deep Learning
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Deep learning radiopathomics predicts targeted therapy sensitivity in EGFR-mutant lung adenocarcinoma.

Journal of translational medicine Apr 29, 2025
BACKGROUND: Ttyrosine kinase inhibitors (TKIs) represent the standard first-line treatment for patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma. However, not all patients with EGFR mutations respond to TKIs. This study...
Deep Learning Female ErbB Receptors Humans Lung Neoplasms Aged Retrospective Studies Mutation Male Tomography, X-Ray Computed Protein Kinase Inhibitors Molecular Targeted Therapy Middle Aged Adenocarcinoma of Lung
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Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Nature communications Apr 15, 2025
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...
Computational Biology Risk Factors Humans Markov Chains Deep Learning Kidney Neoplasms Genetic Predisposition to Disease ErbB Receptors Carcinoma, Renal Cell Reinforcement Machine Learning
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Exploring 4 generation EGFR inhibitors: A review of clinical outcomes and structural binding insights.

European journal of pharmacology Apr 9, 2025
Epidermal growth factor receptor (EGFR) is a potential target for anticancer therapies and plays a crucial role in cell growth, survival, and metastasis. EGFR gene mutations trigger aberrant signaling, leading to non-small cell lung cancer (NSCLC). T...
Protein Kinase Inhibitors Carcinoma, Non-Small-Cell Lung Structure-Activity Relationship Humans Mutation ErbB Receptors Animals Antineoplastic Agents Lung Neoplasms
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