David Graber

This study evaluates how systemic biases affect scoring function performance in virtual screening, finding that biases can inflate results and that classical methods outperform machine learning models on out-of-distribution targets.

The paper introduces GEMS, a graph neural network model that improves binding affinity prediction by removing data leakage and integrating language model embeddings, achieving high performance on independent test datasets.