Matthew Ragoza

According to our database, Matthew Ragoza authored at least 10 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Physics-Informed Neural Networks for Tissue Elasticity Reconstruction in Magnetic Resonance Elastography.

Matthew Ragoza, Kayhan Batmanghelich

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022

Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation.

Yanwu Xu, Shaoan Xie, Maxwell Reynolds, Matthew Ragoza, Mingming Gong, Kayhan Batmanghelich

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021

GNINA 1.0: molecular docking with deep learning.

Andrew T. McNutt, Paul G. Francoeur, Rishal Aggarwal, Tomohide Masuda, Rocco Meli, Matthew Ragoza, Jocelyn Sunseri, David Ryan Koes

J. Cheminformatics, 2021

Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models.

Matthew Ragoza, Tomohide Masuda, David Ryan Koes

CoRR, 2021

2020

Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models.

Tomohide Masuda, Matthew Ragoza, David Ryan Koes

CoRR, 2020

Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models.

Matthew Ragoza, Tomohide Masuda, David Ryan Koes

CoRR, 2020

2018

Visualizing Convolutional Neural Network Protein-Ligand Scoring.

Joshua E. Hochuli, Alec Helbling, Tamar Skaist, Matthew Ragoza, David Ryan Koes

CoRR, 2018

2017

Protein-Ligand Scoring with Convolutional Neural Networks.

Matthew Ragoza, Joshua E. Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes

J. Chem. Inf. Model., 2017

Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks.

Matthew Ragoza, Lillian Turner, David Ryan Koes

CoRR, 2017

2016

A D3R prospective evaluation of machine learning for protein-ligand scoring.

Jocelyn Sunseri, Matthew Ragoza, Jasmine Collins, David Ryan Koes

J. Comput. Aided Mol. Des., 2016