Simon Axelrod

According to our database, Simon Axelrod authored at least 11 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors.

Simon Axelrod, Miroslav Kaspar, Kristýna Jelínková, Markéta Smídková, Erika Bartunková, Sille Stepánová, Eugene Shakhnovich, Václav Kasicka, Martin Dracínský, Zlatko Janeba, Rafael Gómez-Bombarelli

CoRR, April, 2026

2023

Neural scaling of deep chemical models.

Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor W. Coley, Vijay Gadepally

Nat. Mac. Intell., October, 2023

Molecular machine learning with conformer ensembles.

Simon Axelrod, Rafael Gómez-Bombarelli

Mach. Learn. Sci. Technol., September, 2023

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling.

Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli

J. Chem. Inf. Model., September, 2023

Natural Quantum Monte Carlo Computation of Excited States.

David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, James S. Spencer

CoRR, 2023

2022

Learned Force Fields Are Ready For Ground State Catalyst Discovery.

Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin

CoRR, 2022

Thermal half-lives of azobenzene derivatives: virtual screening based on intersystem crossing using a machine learning potential.

Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli

CoRR, 2022

Energy-aware neural architecture selection and hyperparameter optimization.

Nathan C. Frey, Dan Zhao, Simon Axelrod, Michael Jones, David Bestor, Vijay Gadepally, Rafael Gómez-Bombarelli, Siddharth Samsi

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2022

2021

Excited state, non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential.

Simon Axelrod, Eugene Shakhnovich, Rafael Gómez-Bombarelli

CoRR, 2021

2020

GEOM: Energy-annotated molecular conformations for property prediction and molecular generation.

Simon Axelrod, Rafael Gómez-Bombarelli

CoRR, 2020

Differentiable Molecular Simulations for Control and Learning.

Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli

CoRR, 2020