FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical Space
We introduce FragFlex, a flexible-length discrete diffusion model that performs node insertion and deletion at the fragment level.
ProofPaper ↗
Key points
- Drug discovery requires efficient exploration of an astronomically large chemical space.
- Molecular graph diffusion models offer a promising approach to this task, but many require graph size to be fixed before generation.
- Although atom-level insertion and deletion can enable flexible-size generation, jointly modeling molecular size, atom types, and bonds while maintaining chemical validity remains challenging.
- A neural attachment-site predictor and valence-aware assembler then convert the generated fragment graph into an atom-level molecule.
Sources (1)
- [1]FragFlex: Fragment-Based Flexible Molecular Generation for Exploring Vast Chemical SpacearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 02:28 AM
We introduce FragFlex, a flexible-length discrete diffusion model that performs node insertion and deletion at the fragment level.
Drug discovery requires efficient exploration of an astronomically large chemical space.
Extractive summary: sentences quoted from the sources.
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