Accurately Predicting Barrier Heights for Radical Reactions in Solution Using Deep Graph Networks.

Quantitative estimates of reaction barriers and solvent effects are essential for developing kinetic mechanisms and predicting reaction outcomes. Here, we create a new data set of 5,600 unique elementary radical reactions calculated using the M06-2X/def2-QZVP//B3LYP-D3(BJ)/def2-TZVP level of theory. A conformer search is done for each species using TPSS/def2-TZVP.

When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties?

Deep graph neural networks are extensively utilized to predict chemical reactivity and molecular properties. However, because of the complexity of chemical space, such models often have difficulty extrapolating beyond the chemistry contained in the training set. Augmenting the model with quantum mechanical (QM) descriptors is anticipated to improve its generalizability.

ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents.

Predicting Gibbs free energy of solution is key to understanding the solvent effects on thermodynamics and reaction rates for kinetic modeling. Accurately computing solution free energies requires the enumeration and evaluation of relevant solute conformers in solution. However, even after generation of relevant conformers, determining their free energy of solution requires an expensive workflow consisting of several ab initio computational chemistry calculations.

RMG Database for Chemical Property Prediction.

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties.

High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions.

Quantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states.

Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy.

Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP.

Coating nanoparticles with gastric epithelial cell membrane for targeted antibiotic delivery against infection.

Inspired by the natural pathogen-host interactions and adhesion, this study reports on the development of a novel targeted nanotherapeutics for the treatment of () infection. Specifically, plasma membranes of gastric epithelial cells (e.g.

Biomimetic Nanoemulsions for Oxygen Delivery In Vivo.

Blood transfusion is oftentimes required for patients suffering from acute trauma or undergoing surgical procedures in order to help maintain the body's oxygen levels. The continued demand worldwide for blood products is expected to put significant strain on available resources and infrastructure. Unfortunately, efforts to develop viable alternatives to human red blood cells for transfusion are generally unsuccessful.

Erythrocyte membrane-coated nanogel for combinatorial antivirulence and responsive antimicrobial delivery against Staphylococcus aureus infection.

We reported an erythrocyte membrane-coated nanogel (RBC-nanogel) system with combinatorial antivirulence and responsive antibiotic delivery for the treatment of methicillin-resistant Staphylococcus aureus (MRSA) infection. RBC membrane was coated onto the nanogel via a membrane vesicle templated in situ gelation process, whereas the redox-responsiveness was achieved by using a disulfide bond-based crosslinker. We demonstrated that the RBC-nanogels effectively neutralized MRSA-associated toxins in extracellular environment and the toxin neutralization in turn promoted bacterial uptake by macrophages.