AIMC Topic: Thermodynamics

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A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

PloS one Oct 8, 2024
The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agoni...
Molecular Docking Simulation Humans Protein Binding Machine Learning Ligands Molecular Dynamics Simulation Receptors, G-Protein-Coupled Thermodynamics Neurodegenerative Diseases
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AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences Oct 5, 2024
The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, whi...
Neural Networks, Computer Protein Stability Artificial Intelligence Protein Conformation Algorithms Thermodynamics Proteins Models, Molecular
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Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning.

Journal of chemical theory and computation Sep 25, 2024
We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent inte...
Quantum Theory Thermodynamics Machine Learning Molecular Dynamics Simulation
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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...
Markov Chains Dipeptides Thermodynamics Alanine Protein Conformation Machine Learning Molecular Dynamics Simulation
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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...
Dipeptides Protein Conformation Machine Learning Molecular Dynamics Simulation Monte Carlo Method Thermodynamics
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Linear symmetric self-selecting 14-bit kinetic molecular memristors.

Nature Sep 11, 2024
Artificial Intelligence (AI) is the domain of large resource-intensive data centres that limit access to a small community of developers. Neuromorphic hardware promises greatly improved space and energy efficiency for AI but is presently only capable...
Neural Networks, Computer Artificial Intelligence Signal Processing, Computer-Assisted Fourier Analysis Signal-To-Noise Ratio Thermodynamics Kinetics Ligands
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Thermodynamics-inspired explanations of artificial intelligence.

Nature communications Sep 9, 2024
In recent years, predictive machine learning models have gained prominence across various scientific domains. However, their black-box nature necessitates establishing trust in them before accepting their predictions as accurate. One promising strate...
Thermodynamics Entropy Humans Machine Learning Artificial Intelligence Algorithms
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From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Aug 29, 2024
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only ta...
Molecular Dynamics Simulation Ligands HSP90 Heat-Shock Proteins Thermodynamics Protein Binding Proteins Drug Design Deep Learning
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Thermodynamics and explainable machine learning assist in interpreting biodegradability of dissolved organic matter in sludge anaerobic digestion with thermal hydrolysis.

Bioresource technology Aug 28, 2024
Dissolved organic matter (DOM) is essential in biological treatment, yet its specific roles remain incompletely understood. This study introduces a machine learning (ML) framework to interpret DOM biodegradability in the anaerobic digestion (AD) of s...
Carbon Anaerobiosis Machine Learning Organic Chemicals Nitrogen Sewage Hydrolysis Biodegradation, Environmental Thermodynamics
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A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches.

International journal of pharmaceutics Aug 11, 2024
The pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO (scCO)-based techniques. Measuring the solubility of drugs in scCO at varying conditions is a crucial parameter ...
Chemistry, Pharmaceutical Thermodynamics Machine Learning Chlorpromazine Carbon Dioxide Models, Chemical Solubility Febuxostat Solvents
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