David E. Graff

According to our database, David E. Graff authored at least 8 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• 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

Challenging reaction prediction models to generalize to novel chemistry.

John Bradshaw, Anji Zhang, Babak A. Mahjour, David E. Graff, Marwin H. S. Segler, Connor W. Coley

CoRR, January, 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

2023

Pareto Optimization to Accelerate Multi-Objective Virtual Screening.

Jenna C. Fromer, David E. Graff, Connor W. Coley

CoRR, 2023

2022

pyscreener: A Python Wrapper for Computational Docking Software.

David E. Graff, Connor W. Coley

J. Open Source Softw., 2022

Self-Focusing Virtual Screening with Active Design Space Pruning.

David E. Graff, Matteo Aldeghi, Joseph A. Morrone, Kirk E. Jordan, Edward O. Pyzer-Knapp, Connor W. Coley

J. Chem. Inf. Model., 2022

Roughness of Molecular Property Landscapes and Its Impact on Modellability.

Matteo Aldeghi, David E. Graff, Nathan C. Frey, Joseph A. Morrone, Edward O. Pyzer-Knapp, Kirk E. Jordan, Connor W. Coley

J. Chem. Inf. Model., 2022

2020

Accelerating high-throughput virtual screening through molecular pool-based active learning.

David E. Graff, Eugene I. Shakhnovich, Connor W. Coley

CoRR, 2020