Learning Molecular Mixture Property Using Chemistry|Aware Graph ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

We present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

The U.S. Department of Energy's Office of Scientific and Technical Information.

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network. Hengrui Zhang (张恒睿), Tianxing Lai (来天行), Jie Chen ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network. Citation Details. This content will become publicly available on June 1, 2025.

Wei Chen on LinkedIn: Learning Molecular Mixture Property Using ...

Mixing things up and seeing what happens is one thing that makes chemistry so fascinating. Not sure if machines are interested as we are, but ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network. Zhang, H., Lai, T., Chen, J., Manthiram, A., Rondinelli, J. M., & Chen, W.

Molecular Graph Deep Sets Learning for Mixture Property Modeling

The atomic properties and molecular geometry form the essential chemistry to be captured. Owing to its versatility in encoding this information, graphs have ...

Henrium/MolSets: Molecular graph deep sets learning for ... - GitHub

... molecular mixture properties, associated with our paper Learning molecular mixture property using chemistry-aware graph neural network. Model architecture ...

Molecular property prediction based on graph structure learning

Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. Then, with molecular fingerprints, we ...

Chain-aware graph neural networks for molecular property prediction

To get deep insight into the structural features of molecular graphs, we empirically study the local connectivity with clustering coefficient ( ...

Chemprop: A Machine Learning Package for Chemical Property ...

Zhang, S.; Liu, Y.; Xie, L. Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures. Machine Learning for ...

a foundation model for molecular graphs using disentangled attention

... properties based on chemical structure are valuable tools in the chemical sciences. However, for many properties, public and private training ...

Graph Machine Learning: Applications in Molecular Chemistry ...

... property relationships and design new materials with desired properties. Chemoinformatics: Graph machine learning can be used for various ...

Molecular Property Prediction Based on Graph Structure Learning

Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. Then, with molecular ...

Day 1 - Graph Neural Networks for Chemistry | Dominique Beaini

Join Portal to connect with the speakers: https://portal.valencelabs.com/ This is a recording from the 2024 Machine Learning for Drug ...

graph mixup with key structures for molecular property prediction

Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that ...

Machine Learning of Reaction Properties via ... - ACS Publications

A Compact Review of Molecular Property Prediction with Graph Neural Networks. Drug Discovery Today: Technol. 2020, DOI: 10.1016/j.ddtec ...

Fast and effective molecular property prediction with transferability ...

In chemistry, transfer learning leverages pre-trained ... molecular graph representation in both PGM and transfer learning experiments.

Transfer learning with graph neural networks for improved molecular ...

We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high ...