Kevin P. Greenman

According to our database, Kevin P. Greenman authored at least 6 papers between 2023 and 2026.

Collaborative distances:

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

2026

Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction.

David E. Graff, Nathan Morgan, Jackson W. Burns, Anna C. Doner, Brian Li, Shih-Cheng Li, Joel Manu, Angiras Menon, Hao-Wei Pang, Haoyang Wu, Akshat Shirish Zalte, Jonathan W. Zheng, Connor W. Coley, William H. Green Jr., Kevin P. Greenman

J. Chem. Inf. Model., 2026

2025

Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules.

Nofit Segal, Aviv Netanyahu, Kevin P. Greenman, Pulkit Agrawal, Rafael Gómez-Bombarelli

CoRR, February, 2025

Benchmarking uncertainty quantification for protein engineering.

Kevin P. Greenman, Ava P. Amini, Kevin K. Yang

PLoS Comput. Biol., 2025

2024

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green Jr., Charles McGill

J. Chem. Inf. Model., January, 2024

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search.

Akshay Subramanian, James K. Damewood, Juno Nam, Kevin P. Greenman, Avni P. Singhal, Rafael Gómez-Bombarelli

CoRR, 2024

2023

Automated patent extraction powers generative modeling in focused chemical spaces.

Akshay Subramanian, Kevin P. Greenman, Alexis Gervaix, Tzuhsiung Yang, Rafael Gómez-Bombarelli

CoRR, 2023