Thermodynamics

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

PloS one 39378198

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...

Humans Ligands Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Neurodegenerative Diseases Protein Binding Receptors, G-Protein-Coupled Thermodynamics

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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 39490875

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 ...

Adsorption Cellulose Charcoal Chromium Cobalt Ferric Compounds Hydrogen-Ion Concentration Kinetics Machine Learning Manganese Compounds Neural Networks, Computer Oxides Temperature Thermodynamics Wastewater Water Pollutants, Chemical Water Purification

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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 39611994

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...

Amino Acid Sequence Drug Design Humans Machine Learning Molecular Dynamics Simulation Phosphopeptides Protein Binding Protein Domains Protein Serine-Threonine Kinases Thermodynamics

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A Divide-and-Conquer Approach to Nanoparticle Global Optimisation Using Machine Learning.

Journal of chemical information and modeling 39546324

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 Metal Nanoparticles Models, Molecular Nanoparticles Thermodynamics

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AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation 39514694

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-...

Machine Learning Molecular Dynamics Simulation Neural Networks, Computer Proteins Thermodynamics

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Ab initio characterization of protein molecular dynamics with AIBMD.

Nature 39506110

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...

Artificial Intelligence Density Functional Theory Machine Learning Molecular Dynamics Simulation Nuclear Magnetic Resonance, Biomolecular Peptides Protein Conformation Protein Folding Protein Unfolding Proteins Quantum Theory Thermodynamics Time Factors

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Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective.

The journal of physical chemistry. B 39484814

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...

Antiviral Agents Dengue Virus Machine Learning Molecular Dynamics Simulation Protease Inhibitors Serine Endopeptidases Thermodynamics Viral Protease Inhibitors Viral Proteases

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Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding.

Journal of chemical information and modeling 39469831

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...

Density Functional Theory Halogens Machine Learning Models, Molecular Static Electricity Thermodynamics

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Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

Journal of chemical information and modeling 39441973

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,...

Deep Learning Kinetics Monte Carlo Method Thermodynamics Uncertainty

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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 39617885

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...

Allosteric Regulation Epistasis, Genetic Humans Machine Learning Models, Genetic Mutation Neural Networks, Computer Software Thermodynamics

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Explore the latest research on artificial intelligence and machine learning in medicine.

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

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).

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.

A Divide-and-Conquer Approach to Nanoparticle Global Optimisation Using Machine Learning.

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Ab initio characterization of protein molecular dynamics with AIBMD.

Machine-Learning Approach to Identify Potential Dengue Virus Protease Inhibitors: A Computational Perspective.

Data and Molecular Fingerprint-Driven Machine Learning Approaches to Halogen Bonding.

Uncertainty Qualification for Deep Learning-Based Elementary Reaction Property Prediction.

MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

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