Summary of Turbohopp: Accelerated Molecule Scaffold Hopping with Consistency Models, by Kiwoong Yoo et al.
TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
by Kiwoong Yoo, Owen Oertell, Junhyun Lee, Sanghoon Lee, Jaewoo Kang
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce TurboHopp, a new generative model that accelerates pocket-conditioned 3D scaffold hopping for efficient drug discovery. By merging the strengths of traditional scaffold hopping with consistency models, TurboHopp achieves up to 30 times faster inference speed and superior generation quality compared to existing diffusion-based models. The authors demonstrate the broad applicability of TurboHopp across multiple drug discovery scenarios, highlighting its potential in diverse molecular settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TurboHopp is a new tool that helps scientists find new medicines by quickly generating ideas for molecules that could work as drugs. This paper shows how to make this process faster and better by combining two different approaches. The result is a model that can generate many ideas quickly, which will be very useful in the search for new medicines. |
Keywords
» Artificial intelligence » Diffusion » Generative model » Inference
