Results 171 to 180 of about 12,385,978 (300)

Reorienting Allied Health Into Community-Based Care for People Experiencing Trauma and Social Disadvantage. [PDF]

open access: yesMed J Aust
Rosenbaum S   +35 more
europepmc   +1 more source

Machine learning–driven design of catalytic processes for sulfur dioxide oxidation: Lessons from the trenches

open access: yesAIChE Journal, EarlyView.
Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin   +2 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

open access: yesAdvanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

open access: yesAdvanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa   +2 more
wiley   +1 more source

A 3D Multitask Graph Neural Network Framework for Ames Mutagenicity Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
Our proposed multitask graph neural network model has a high predictive accuracy for Ames mutagenicity. By operating directly on molecular graph representations instead of traditional molecular descriptors, and predicting strain‐specific results, the model outperforms most existing benchmarks.
Abigail E. Teitgen   +3 more
wiley   +1 more source

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