AIMC Topic: Thermodynamics

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Application of machine learning approaches for estimating carbon dioxide absorption capacity of a variety of blended imidazolium-based ionic liquids.

Journal of molecular graphics & modelling Apr 26, 2025
Ionic liquids (ILs) have gained attention in recent times as potentially effective absorbents for CO emissions owing to the number of their notable attributes, including reduced volatility, enhanced thermal consistency etc. Due to the number of chall...
Machine Learning Imidazoles Ionic Liquids Thermodynamics Carbon Dioxide Solubility
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Improved Solubility Predictions in scCO Using Thermodynamics-Informed Machine Learning Models.

Journal of chemical information and modeling Apr 15, 2025
Accurate solubility prediction in supercritical carbon dioxide (scCO) is crucial for optimizing experimental design by eliminating unnecessary and costly trials at an early stage, thereby streamlining the workflow. A comprehensive solubility database...
Machine Learning Carbon Dioxide Solubility Thermodynamics
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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.

Journal of chemical information and modeling Apr 8, 2025
Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we ...
Quantum Theory Ligands Proteins Thermodynamics Neural Networks, Computer Molecular Dynamics Simulation Protein Binding
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Optimizing Cu adsorption prediction in Undaria pinnatifida using machine learning and isotherm models.

Journal of hazardous materials Apr 7, 2025
Algae are cost-effective bioadsorbents for heavy metal remediation, yet their potential is underutilized due to limitations in traditional adsorption models. This study integrates machine learning (ML) techniques with traditional models to predict th...
Machine Learning Water Pollutants, Chemical Kinetics Edible Seaweeds Adsorption Copper Thermodynamics Undaria
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HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions.

Journal of chemical information and modeling Apr 4, 2025
Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water's remarkable versatility as a solvent. However, accurately predicting...
Graph Neural Networks Hydrolysis Thermodynamics Hydrogen-Ion Concentration Neural Networks, Computer
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Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

Molecular diversity Apr 2, 2025
Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certai...
Receptors, Cytoplasmic and Nuclear Thermodynamics Deep Learning Protein Binding Molecular Dynamics Simulation Hydrophobic and Hydrophilic Interactions Humans Drug Discovery
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Recent Advances in the Modeling of Ionic Liquids Using Artificial Neural Networks.

Journal of chemical information and modeling Mar 27, 2025
This paper reviews the recent and most impactful advancements in the application of artificial neural networks in modeling the properties of ionic liquids. As salts that are liquid at temperatures below 100 °C, ionic liquids possess unique properties...
Ionic Liquids Neural Networks, Computer Thermodynamics
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Insights into phosphorylation-induced influences on conformations and inhibitor binding of CDK6 through GaMD trajectory-based deep learning.

Physical chemistry chemical physics : PCCP Mar 26, 2025
The phosphorylation of residue T177 produces a significant effect on the conformational dynamics of CDK6. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) are applied to explore the molecular mechanism of the ...
Humans Deep Learning Thermodynamics Molecular Dynamics Simulation Cyclin-Dependent Kinase 6 Protein Conformation Protein Kinase Inhibitors Protein Binding Phosphorylation
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Current experimental, statistical, and mechanistic approaches to optimizing biomolecule separations in aqueous two-phase systems.

Journal of chromatography. A Mar 17, 2025
Aqueous two-phase systems (ATPS) have been used to purify a range of biomolecules, including small molecules, monoclonal antibodies, viruses, and whole cells. They are known for selective separations, creating a stabilizing, low-shear environment, an...
Models, Chemical Thermodynamics Water Models, Statistical Proteins
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Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network.

Journal of chemical theory and computation Mar 16, 2025
Tautomerization plays a critical role in chemical and biological processes, influencing molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solution, c...
Neural Networks, Computer Solutions Water Deep Learning Isomerism Thermodynamics
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