Summary of Double-ended Synthesis Planning with Goal-constrained Bidirectional Search, by Kevin Yu et al.
Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
by Kevin Yu, Jihye Roh, Ziang Li, Wenhao Gao, Runzhong Wang, Connor W. Coley
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Quantitative Methods (q-bio.QM)
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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 A novel Computer-aided Synthesis Planning (CASP) algorithm called Double-Ended Synthesis Planning (DESP) is proposed to address the constraint of starting material limitations in synthesis planning. The algorithm, based on a bidirectional graph search scheme, interleaves expansions from the target and goal starting materials to ensure constraint satisfiability. DESP uses a goal-conditioned cost network learned offline from a hypergraph of valid chemical reactions to guide its search. This approach improves solve rates and reduces search expansions by biasing synthesis planning towards expert goals on multiple new benchmarks. The algorithm can leverage existing one-step retrosynthesis models, and its performance is expected to scale with improving model capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Computer-aided synthesis planning helps scientists design new molecules. Current algorithms are good at finding the right pieces (building blocks) to make a molecule, but they don’t consider when specific starting materials are needed. To fix this, researchers developed a new algorithm called Double-Ended Synthesis Planning (DESP). DESP looks at both the target molecule and the desired starting materials to find a way to connect them. This approach helps solve problems faster and uses information from existing models. |
