1 citations · 1 across the 2 of their papers we have counts for
3 papers
Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery
Ya Ju Fan, Jonathan E. Allen, Kevin S. McLoughlin +4
Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models require uncertainty quantification (UQ) a…
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models
Garrett A. Stevenson, Derek Jones, Hyojin Kim +29
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a mu…
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
Derek Jones, Hyojin Kim, Xiaohua Zhang +7
Predicting accurate protein-ligand binding affinity is important in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring metho…