Research Topic

graph neural

6 publications exploring this topic

2025

2025

Democratizing Reaction Kinetics through Machine Vision and Learning

Baumer M., Gallegos L., Anstine D., Kubaney A., Regio J., Isayev O., Bernhard S., Gomes G.

(2025)

Reactions Reactivity
Ml Potentials

Democratizing Reaction Kinetics through Machine Vision and Learning.

DOI

2024

2024
cited2

Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions

Liu Z., Vinskus J., Fu Y., Liu P., Noonan K., Isayev O.

(2024)

Ml Potentials
Experiment Automation
Reactions Reactivity

Ring strain energy (RSE) is crucial for understanding molecular reactivity.

DOI

2020

2020

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Reactions Reactivity

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DOI
2020

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Reactions Reactivity

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DOI
2020
cited7

DRACON: Disconnected Graph Neural Network for Atom Mapping in Chemical Reactions

Nikitin F., Isayev O., Strijov V.

(2020)

Reactions Reactivity

Machine learning solved many challenging problems in computer-assisted synthesis prediction (CASP).

DOI
2020
cited9

DRACON: disconnected graph neural network for atom mapping in chemical reactions

Nikitin F., Isayev O., Strijov V.

Physical Chemistry Chemical Physics, 22, 26478–26486 (2020)

Reactions Reactivity

We formulate a reaction prediction problem in terms of node-classification in a disconnected graph of source molecules and generalize a graph convolution neural network for disconnected graphs.

DOI