Summary of Synflownet: Design Of Diverse and Novel Molecules with Synthesis Constraints, by Miruna Cretu et al.
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
by Miruna Cretu, Charles Harris, Ilia Igashov, Arne Schneuing, Marwin Segler, Bruno Correia, Julien Roy, Emmanuel Bengio, Pietro Liò
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper proposes a novel generative model called SynFlowNet, which addresses the issue of producing synthetically inaccessible molecules in computer-aided drug design. By incorporating forward synthesis as an explicit constraint, the model aims to bridge the gap between in silico molecular generation and real-world synthesis capabilities. The approach uses chemical reactions and buyable reactants to sequentially build new molecules, improving sample diversity compared to baselines. The paper evaluates SynFlowNet using synthetic accessibility scores and an independent retrosynthesis tool, demonstrating its ability to identify synthesis pathways for previously unseen molecules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a computer program better at creating new medicine ideas that can actually be made in a lab. It’s like a game where the program tries out different ways to make new medicines, but it also has rules to make sure what it comes up with is something scientists could really do. This helps make the program more useful for finding new medicines. |
Keywords
» Artificial intelligence » Generative model
