Guiding large language models to predict edit sequences for molecular synthesizability optimization
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TL;DR - Li and Lai use large language models to predict structural edit sequences that make computationally designed molecules more synthesizable. The approach reportedly outperforms traditional methods while preserving important molecular features.
- Targets a key drug-design failure mode: generated molecules that cannot be practically synthesized.
- Predicts precise, sequential modifications to molecular structures rather than merely scoring synthesizability.
- Aims to improve synthesizability without sacrificing key properties of the original candidate.
- The provided summary reports better performance than traditional optimization methods but does not include quantitative results.
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Guiding large language models to predict edit sequences for molecular synthesizability optimization
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TL;DR - Li and Lai use large language models to predict structural edit sequences that make computationally designed molecules more synthesizable. The approach reportedly outperforms traditional methods while preserving important molecular features.
- Targets a key drug-design failure mode: generated molecules that cannot be practically synthesized.
- Predicts precise, sequential modifications to molecular structures rather than merely scoring synthesizability.
- Aims to improve synthesizability without sacrificing key properties of the original candidate.
- The provided summary reports better performance than traditional optimization methods but does not include quantitative results.