AIMC Topic: Thermodynamics

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Limits on the computational expressivity of non-equilibrium biophysical processes.

Nature communications Aug 5, 2025
Many biological decision-making tasks require classifying high-dimensional chemical states. The biophysical and computational mechanisms that enable classification remain enigmatic. In this work, using Markov jump processes as an abstraction of gener...
Neural Networks, Computer Computer Simulation Biophysical Phenomena Thermodynamics Models, Biological Markov Chains
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Thermodynamic analysis and intelligent modeling of statin drugs solubility in supercritical carbon dioxide.

Scientific reports Aug 2, 2025
Evaluating the solubility of various drugs in supercritical CO is a fundamental step in developing a supercritical process for formulating new pharmaceuticals. Atorvastatin, Lovastatin, and Simvastatin are statin drugs with limited solubility and low...
Atorvastatin Thermodynamics Carbon Dioxide Solubility Hydroxymethylglutaryl-CoA Reductase Inhibitors Simvastatin Neural Networks, Computer Machine Learning
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Navigating protein landscapes with a machine-learned transferable coarse-grained model.

Nature chemistry Jul 18, 2025
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar pr...
Molecular Dynamics Simulation Protein Conformation Machine Learning Proteins Thermodynamics Protein Folding
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Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants.

Journal of chemical information and modeling Jul 17, 2025
With the advancement of artificial intelligence-embedded methodologies, their application to predict fundamental molecular properties has become increasingly prevalent. In this study, a graph convolutional neural network fingerprint-enabled artificia...
Thermodynamics Neurotransmitter Agents Models, Molecular Antidepressive Agents Humans Neural Networks, Computer Convolutional Neural Networks
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State Ensemble Energy Recognition (SEER): A Hybrid Gas-Phase Molecular Charge State Predictor.

Journal of chemical information and modeling Jul 8, 2025
Accurately resolving a three-dimensional structure that corresponds to an experimental mass spectrometry (MS) result is valuable for outcomes such as improved analyte identification, determination of physiochemical properties relating to conformation...
Thermodynamics Models, Molecular Gases Software Mass Spectrometry Machine Learning
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Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements.

Journal of chemical information and modeling Jul 7, 2025
Machine learning potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable data sets to ensure robust generalization across ...
Silicon Thermodynamics Germanium Machine Learning
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Combination of Molecular Dynamics Simulations and Machine Learning Reveals Structural Characteristics of Stereochemistry-Specific Interdigitation of Synthetic Monomycoloyl Glycerol Analogs.

Journal of chemical information and modeling Jul 3, 2025
Synthetic monomycoloyl glycerol (MMG) analogs possess robust immunostimulatory activity and are investigated as adjuvants for subunit vaccines in preclinical and clinical studies. These synthetic lipids consist of a glycerol moiety attached to a cory...
Machine Learning Thermodynamics Glycerol Molecular Dynamics Simulation Stereoisomerism Molecular Conformation
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Prediction of thermodynamic properties of aqueous carbohydrates solution using the PHSC and ANN models.

Scientific reports Jul 1, 2025
In this work the Artificial Neural Network (ANN) and the Perturbed Hard Sphere Chain (PHSC) equation of state (EoS) have been utilized to estimate the osmotic coefficient, activity coefficient, and water activity of aqueous sugar solutions containing...
Water Carbohydrates Neural Networks, Computer Solutions Thermodynamics
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Deep generalizable prediction of RNA secondary structure via base pair motif energy.

Nature communications Jul 1, 2025
Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and r...
Thermodynamics Deep Learning Base Pairing Computational Biology Nucleotide Motifs RNA Nucleic Acid Conformation
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Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Science advances Jun 11, 2025
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...
Thermodynamics Mutation Kinetics Amyloid beta-Peptides Alzheimer Disease Humans Peptide Fragments Protein Aggregates Machine Learning
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