AIMC Topic: Mitogen-Activated Protein Kinase 14

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Binding mechanism of inhibitors to DFG-in and DFG-out P38α deciphered using multiple independent Gaussian accelerated molecular dynamics simulations and deep learning.

SAR and QSAR in environmental research Mar 20, 2025
P38α has been identified as a key target for drug design to treat a wide range of diseases. In this study, multiple independent Gaussian accelerated molecular dynamics (GaMD) simulations, deep learning (DL), and the molecular mechanics generalized Bo...
Drug Design Deep Learning Protein Binding Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Mitogen-Activated Protein Kinase 14
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TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network.

BMC bioinformatics May 31, 2017
BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimen...
Machine Learning Animals Transcriptome Gene Regulatory Networks Mice Transcription Factors Mice, Knockout Binding Sites Mitogen-Activated Protein Kinase 14
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Visualization and Interpretation of Support Vector Machine Activity Predictions.

Journal of chemical information and modeling Jun 2, 2015
Support vector machines (SVMs) are among the preferred machine learning algorithms for virtual compound screening and activity prediction because of their frequently observed high performance levels. However, a well-known conundrum of SVMs (and other...
Humans Software Algorithms Support Vector Machine Drug Evaluation, Preclinical Ligands Linear Models Protein Kinase Inhibitors Receptors, Dopamine D2 Mitogen-Activated Protein Kinase 14 Receptor, Cannabinoid, CB2
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