9 citations · 11 across the 5 of their papers we have counts for
8 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…
A cross-study analysis of drug response prediction in cancer cell lines
Fangfang Xia, Jonathan Allen, Prasanna Balaprakash +21
To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm deve…
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…
Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump
Kevin S. McLoughlin, Claire G. Jeong, Thomas D. Sweitzer +7
Drug-induced liver injury (DILI) is the most common cause of acute liver failure and a frequent reason for withdrawal of candidate drugs during preclinical and clinical testing. An…
Split Optimization for Protein/Ligand Binding Models
Brian Davis, Kevin Mcloughlin, Jonathan Allen +1
In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymm…