AIMC Topic: Dipeptides

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Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.

European journal of radiology Mar 19, 2025
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...
Positron Emission Tomography Computed Tomography Prostatic Neoplasms Prostate-Specific Antigen Middle Aged Aged Risk Assessment Aged, 80 and over Gallium Radioisotopes Biomarkers, Tumor Gallium Isotopes Male Heterocyclic Compounds, 1-Ring Humans Sensitivity and Specificity Radiopharmaceuticals Machine Learning Dipeptides Retrospective Studies
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Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.

Journal of chemical theory and computation Nov 26, 2024
The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional colle...
Humans Deep Learning Molecular Dynamics Simulation Dipeptides Oligopeptides
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Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Journal of chemical theory and computation Sep 20, 2024
Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they...
Alanine Molecular Dynamics Simulation Markov Chains Protein Conformation Thermodynamics Dipeptides Machine Learning
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A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

Journal of chemical theory and computation Sep 17, 2024
The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and m...
Molecular Dynamics Simulation Dipeptides Protein Conformation Machine Learning Monte Carlo Method Thermodynamics
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Extended dipeptide composition framework for accurate identification of anticancer peptides.

Scientific reports Jul 29, 2024
The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorpora...
Dipeptides Computational Biology Support Vector Machine Humans Machine Learning Reproducibility of Results Algorithms Antineoplastic Agents
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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation Jun 18, 2024
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...
Neural Networks, Computer Proteins Protein Conformation Static Electricity Thermodynamics Molecular Dynamics Simulation Dipeptides
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NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling May 25, 2024
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...
Dipeptides Protein Conformation Monte Carlo Method Proteins Molecular Dynamics Simulation Markov Chains Algorithms Deep Learning
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Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions.

The journal of physical chemistry. A Jan 17, 2024
We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a cor...
Neural Networks, Computer Dipeptides Molecular Conformation
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PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy.

Analytical biochemistry Oct 4, 2022
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating c...
Amino Acids Dipeptides Arabidopsis Ubiquitination Deep Learning Ubiquitinated Proteins
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A New Clinical Utility for Tubular Markers to Identify Kidney Responders to Saxagliptin Treatment in Adults With Diabetic Nephropathy.

Canadian journal of diabetes Aug 2, 2021
OBJECTIVES: In recent clinical studies, saxagliptin exhibited nephroprotective potential by lowering albuminuria. In this study, we aimed to determine whether these kidney effects of saxagliptin were mediated by changes in markers of kidney tubular d...
Dipeptides Diabetic Nephropathies Adult Humans Kidney Glomerular Filtration Rate Diabetes Mellitus, Type 2 Biomarkers Albuminuria Adamantane
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