LEARNING MATTER

Simon Axelrod

Year of Publication:
2023
Mapping the space of photoswitchable ligands and photodruggable proteins with computational modeling

J. Chem. Inf. Model.
,

2023
Year of Publication:
2023
Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential

ACS Central Science
,

2023
Year of Publication:
2022
Neural Scaling of Deep Chemical Models

ChemRxiv
,

2022
Year of Publication:
2022
Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

Nature Communications
,

vol.
13
,

pp.
3440
,

2022
Year of Publication:
2022
Learning Matter: Materials Design with Machine Learning and Atomistic Simulations

Accounts of Materials Research
,

2022
Year of Publication:
2022
Bottlebrush polymers with flexible enantiomeric side chains display differential biological properties

Nature Chemistry
,

vol.
14
,

pp.
85-93
,

2022
Year of Publication:
2021
Synthetic Glycomacromolecules of Defined Valency, Absolute Configuration, and Topology Distinguish between Human Lectins

JACS Au
,

vol.
1(10)
,

pp.
1621–1630
,

2021
Year of Publication:
2023
- Simon Axelrod
- Rafael Gómez-Bombarelli
Molecular machine learning with conformer ensembles

Machine Learning: Science and Technology
,

2023
Year of Publication:
2020
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks

The Journal of Chemical Physics
,

vol.
153
,

pp.
164501
,

2020
Year of Publication:
2022
- Simon Axelrod
- Rafael Gómez-Bombarelli
GEOM: Energy-annotated molecular conformations for property prediction and molecular generation

Scientific Data
,

vol.
9
,

pp.
185
,

2022