Protein Kinase Inhibitors

Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.

Journal of biomolecular structure & dynamics Jan 10, 2024

Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a c...

Machine Learning T-Lymphocytes Molecular Docking Simulation Humans Molecular Dynamics Simulation Signal Transduction Protein Kinase Inhibitors Protein Serine-Threonine Kinases Thermodynamics Structure-Activity Relationship Drug Discovery Protein Binding

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T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study.

Journal of biomolecular structure & dynamics Jan 4, 2024

Acquired resistance to EGFR is a major impediment in lung cancer treatment, highlighting the urgent need to discover novel compounds to overcome EGFR drug resistance. In this study, we utilized in silico methods and bioactivity evaluation for drug di...

Cell Line, Tumor Protein Binding Cell Proliferation Drug Discovery Antineoplastic Agents Humans Machine Learning Molecular Docking Simulation Apoptosis Drug Screening Assays, Antitumor Mutation Drug Resistance, Neoplasm ErbB Receptors Protein Kinase Inhibitors

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Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Journal of biomolecular structure & dynamics Dec 14, 2023

Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are sear...

Molecular Dynamics Simulation High-Throughput Screening Assays Quantitative Structure-Activity Relationship Protein Binding Proto-Oncogene Proteins c-akt Protein Kinase Inhibitors Allosteric Regulation Humans Machine Learning Molecular Docking Simulation Drug Discovery

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Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.

PLoS computational biology Sep 5, 2023

Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine learning pipeline to unravel the target landscape of ...

Protein Kinase Inhibitors Machine Learning Drug Delivery Systems Workflow Neural Networks, Computer

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A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

Journal of chemical information and modeling Aug 18, 2023

Kinases have been the focus of drug discovery programs for three decades leading to over 70 therapeutic kinase inhibitors and biophysical affinity measurements for over 130,000 kinase-compound pairs. Nonetheless, the precise target spectrum for many ...

Protein Kinase Inhibitors Machine Learning Drug Discovery

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FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Molecular diversity Aug 11, 2023

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cellular functions (translocating proteins, tissue repair, cell proliferation, development, and differen...

Receptor, Fibroblast Growth Factor, Type 1 Humans Artificial Intelligence Drug Discovery Antineoplastic Agents Protein Kinase Inhibitors

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Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability.

Molecular diversity Jul 21, 2023

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. T...

Humans Neural Networks, Computer Machine Learning Algorithms Support Vector Machine Tyrosine Kinase Inhibitors Protein Kinase Inhibitors Agammaglobulinaemia Tyrosine Kinase

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AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.

Scientific reports Jun 24, 2023

The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for...

Deep Learning Ligands Protein Kinase Inhibitors Drug Design Sequence Alignment Eye, Artificial

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Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

ACS sensors Jun 6, 2023

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutate...

Exosomes Deep Learning Humans ErbB Receptors Lung Neoplasms Carcinoma, Non-Small-Cell Lung Protein Kinase Inhibitors Spectrum Analysis, Raman Mutation

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Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis.

Journal of biomolecular structure & dynamics May 26, 2023

The rise of antibiotic-resistant Mycobacterium tuberculosis (Mtb) has reduced the availability of medications for tuberculosis therapy, resulting in increased morbidity and mortality globally. Tuberculosis spreads from the lungs to other parts of the...

Nucleoside-Phosphate Kinase Protein Binding Antitubercular Agents Molecular Dynamics Simulation Algorithms Humans Mycobacterium tuberculosis Molecular Docking Simulation Machine Learning Protein Kinase Inhibitors Drug Design

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Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.

T6496 targeting EGFR mediated by T790M or C797S mutant: machine learning, virtual screening and bioactivity evaluation study.

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.

A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability.

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning.

Machine learning-based drug design for identification of thymidylate kinase inhibitors as a potential anti-Mycobacterium tuberculosis.

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