Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results

Autoparty: Machine Learning-Guided Visual Inspection of Molecular Docking Results

Human inspection of potential drug compounds is crucial in the virtual drug screening pipeline. However, there is a pressing need to accelerate this process, as the number of molecules humans can realistically examine is extremely limited relative to the scale of virtual screens. Furthermore, comput...

Full description

Saved in:
Bibliographic Details
Main Author: Laura Shub (12700066) (author)
Other Authors: Magdalena Korczynska (1327899) (author), Duncan F. Muir (21808181) (author), Fang-Yu Lin (730570) (author), Brendan W. Hall (21808184) (author), Alan M. Mathiowetz (1348374) (author), Michael J. Keiser (260659) (author)
Published: 2025
Subjects:
Neuroscience
Pharmacology
Mental Health
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
world case study
guided visual inspection
extremely limited relative
exportable local database
computational medicinal chemists
broad downstream uses
premises active learning
potential drug compounds
extrapolate human intuition
collected annotations populate
incorporating autoparty resulted
recording annotations
machine learning
facilitate human
drug discovery
virtual screens
standard way
realistically examine
propose autoparty
pressing need
molecules humans
model training
loop training
informative examples
hit rate
containerized tool
autoparty leverages
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items