DockStream: a docking wrapper to enhance de novo molecular design
importance 6/10
ai assisted
Computational Drug Discovery Methods drug discovery stage: research
Problem
Generative molecular design models struggle to produce compounds that are actually active against a biological target.
Why it matters
Producing active drug candidates faster could cut the cost and time of small-molecule drug discovery.
What the AI did
DockStream adds a structure-based molecular docking scoring component to the REINVENT generative design platform, with automated benchmarking and analysis of docking configurations.
Results
not stated in source
Impact
Provides a tool enabling structure-based scoring inside de novo molecular design workflows.
Record
| Venue | Journal of Cheminformatics |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Jeff Guo, Jon Paul Janet, Matthias R. Bauer, Eva Nittinger, Kathryn A. Giblin, Kostas Papadopoulos, Alexey Voronov, Atanas Patronov et al. |
| Citations | 76 (OpenAlex, at import time) |
| References | 82 |
| Model or method | REINVENT 2.0 generative platform with DockStream docking wrapper |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s13321-021-00563-7 |
| Open access | gold |
| Keywords | molecular docking, de novo design, generative models, REINVENT, drug discovery |
Importance rating
6
significant — thousands to millions affected
Abstract calls producing active compounds a major obstacle for generative models in small-molecule drug discovery; no scale stated.