Jack Scantlebury

This paper evaluates machine learning-based virtual screening models' ability to identify functional groups responsible for binding using synthetic data and finds that deep learning models outperform traditional methods.

The paper develops a machine learning scoring function for virtual screening that avoids dataset biases and demonstrates its ability to identify important binding interactions through input attribution.

This paper shows that dataset augmentation improves deep learning models for virtual screening by making them better at generalizing to new protein targets and highlighting important binding interactions.