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Joseph A. Morrone
ORCID
Publication Activity (10 Years)
Years Active: 2020-2022
Publications (10 Years): 8
Top Topics
Generation Method
Small Number
Convolutional Neural Networks
Virtual Screening
Top Venues
J. Chem. Inf. Model.
CoRR
J. Comput. Aided Mol. Des.
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Publications
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David E. Graff
,
Matteo Aldeghi
,
Joseph A. Morrone
,
Kirk E. Jordan
,
Edward O. Pyzer-Knapp
,
Connor W. Coley
Self-focusing virtual screening with active design space pruning.
CoRR
(2022)
Jeffrey K. Weber
,
Joseph A. Morrone
,
Sugato Bagchi
,
Jan D. Estrada Pabon
,
Seung-gu Kang
,
Leili Zhang
,
Wendy D. Cornell
Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.
J. Comput. Aided Mol. Des.
36 (5) (2022)
Seung-gu Kang
,
Jeffrey K. Weber
,
Joseph A. Morrone
,
Leili Zhang
,
Tien Huynh
,
Wendy D. Cornell
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation.
CoRR
(2022)
David E. Graff
,
Matteo Aldeghi
,
Joseph A. Morrone
,
Kirk E. Jordan
,
Edward O. Pyzer-Knapp
,
Connor W. Coley
Self-Focusing Virtual Screening with Active Design Space Pruning.
J. Chem. Inf. Model.
62 (16) (2022)
Matteo Aldeghi
,
David E. Graff
,
Nathan C. Frey
,
Joseph A. Morrone
,
Edward O. Pyzer-Knapp
,
Kirk E. Jordan
,
Connor W. Coley
Roughness of Molecular Property Landscapes and Its Impact on Modellability.
J. Chem. Inf. Model.
62 (19) (2022)
Seung-gu Kang
,
Joseph A. Morrone
,
Jeffrey K. Weber
,
Wendy D. Cornell
Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation.
J. Chem. Inf. Model.
62 (4) (2022)
Seung-gu Kang
,
Joseph A. Morrone
,
Jeffrey K. Weber
,
Wendy D. Cornell
Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder - Insights for practical AI-driven molecule generation.
CoRR
(2021)
Joseph A. Morrone
,
Jeffrey K. Weber
,
Tien Huynh
,
Heng Luo
,
Wendy D. Cornell
Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach.
J. Chem. Inf. Model.
60 (9) (2020)