1 citations · 1 across the 3 of their papers we have counts for
5 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…
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…
AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
Amanda J. Minnich, Kevin McLoughlin, Margaret Tse +9
One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation pro…