8 citations · 9 across the 5 of their papers we have counts for
6 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…
Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning
Fikret Aydin, Konstantia Georgouli, Gautham Dharuman +5
Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the m…
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
Konstantia Georgouli, Helgi I Ingólfsson, Fikret Aydin +4
Capturing intricate biological phenomena often requires multiscale modeling where coarse and inexpensive models are developed using limited components of expensive and high-fidelit…
Dynamic Density Functional Theory of Multicomponent Cellular Membranes
Liam G. Stanton, Tomas Oppelstrup, Timothy S. Carpenter +4
We present a continuum model trained on molecular dynamics (MD) simulations for cellular membranes composed of an arbitrary number of lipid types. The model is constructed within t…
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…