1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
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…
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency
Ya Ju Fan, Jonathan E. Allen, Sam Ade Jacobs +1
Gene expression profiles have been widely used to characterize patterns of cellular responses to diseases. As data becomes available, scalable learning toolkits become essential to…