AIMC Topic: Thermodynamics

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Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.

Journal of chemical information and modeling Dec 3, 2024
We present an artificial intelligence-guided approach to design durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs a genetic algorithm (GA) that designs new monomers and then utilizes virtual for...
Polymerization Thermodynamics Machine Learning Algorithms Plastics Polystyrenes Recycling
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MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

Genome biology Dec 2, 2024
We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data...
Humans Neural Networks, Computer Machine Learning Thermodynamics Epistasis, Genetic Allosteric Regulation Software Models, Genetic Mutation
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Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

European biophysics journal : EBJ Nov 29, 2024
Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localizati...
Phosphopeptides Molecular Dynamics Simulation Amino Acid Sequence Thermodynamics Machine Learning Protein Serine-Threonine Kinases Drug Design Humans Protein Domains Protein Binding
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A Divide-and-Conquer Approach to Nanoparticle Global Optimisation Using Machine Learning.

Journal of chemical information and modeling Nov 15, 2024
Global optimization of the structure of atomic nanoparticles is often hampered by the presence of many funnels on the potential energy surface. While broad funnels are readily encountered and easily exploited by the search, narrow funnels are more di...
Machine Learning Nanoparticles Models, Molecular Metal Nanoparticles Thermodynamics
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AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation Nov 8, 2024
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-...
Proteins Molecular Dynamics Simulation Neural Networks, Computer Thermodynamics Machine Learning
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Ab initio characterization of protein molecular dynamics with AIBMD.

Nature Nov 6, 2024
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...
Protein Folding Protein Unfolding Protein Conformation Nuclear Magnetic Resonance, Biomolecular Molecular Dynamics Simulation Artificial Intelligence Thermodynamics Quantum Theory Machine Learning Peptides Time Factors Proteins Density Functional Theory
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Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective.

The journal of physical chemistry. B Nov 1, 2024
The global prevalence of dengue virus (DENV), a widespread flavivirus, has led to varied epidemiological impacts, economic burdens, and health consequences. The alarming increase in infections is exacerbated by the absence of approved antiviral agent...
Thermodynamics Protease Inhibitors Viral Proteases Dengue Virus Serine Endopeptidases Viral Protease Inhibitors Machine Learning Antiviral Agents Molecular Dynamics Simulation
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Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding.

Journal of chemical information and modeling Oct 29, 2024
The ability to predict the strength of halogen bonds and properties of halogen bond (XB) donors has significant utility for medicinal chemistry and materials science. XBs are typically calculated through expensive ab initio methods. Thus, the develop...
Models, Molecular Static Electricity Thermodynamics Machine Learning Density Functional Theory Halogens
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Biosorption of cobalt and chromium from wastewater using manganese dioxide and iron oxide nanoparticles loaded on cellulose-based biochar: Modeling and optimization with machine learning (artificial neural network).

International journal of biological macromolecules Oct 28, 2024
In this study, two nanomaterials with excellent adsorption capacities were developed to remove heavy metals efficiently from wastewater. Manganese dioxide MnO nanoparticles and iron oxide FeO nanoparticles were successfully synthesized using cassava ...
Kinetics Hydrogen-Ion Concentration Cobalt Charcoal Chromium Cellulose Water Pollutants, Chemical Adsorption Manganese Compounds Oxides Wastewater Neural Networks, Computer Water Purification Machine Learning Temperature Ferric Compounds Thermodynamics
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Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

Journal of chemical information and modeling Oct 23, 2024
The prediction of the thermodynamic and kinetic properties of elementary reactions has shown rapid improvement due to the implementation of deep learning (DL) methods. While various studies have reported the success in predicting reaction properties,...
Uncertainty Kinetics Monte Carlo Method Thermodynamics Deep Learning
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