6 citations · 13 across the 4 of their papers we have counts for
4 papers
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
Peter Eckmann, Dongxia Wu, Germano Heinzelmann +2
Current generative models for drug discovery primarily use molecular docking as an oracle to guide the generation of active compounds. However, such models are often not useful in…
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling
Peter Eckmann, Dongxia Wu, Germano Heinzelmann +2
Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds. However, such models are often not useful in practice b…
Target-Free Compound Activity Prediction via Few-Shot Learning
Peter Eckmann, Jake Anderson, Michael K. Gilson +1
Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discove…
Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields
Chapin E. Cavender, David A. Case, Julian C. -H. Chen +11
This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear ma…