arXiv Artificial Intelligence

MolWorld: Molecule World Models for Actionable Molecular Optimization

MolWorld: Molecule World Models for Actionable Molecular Optimization

Quick summary

arXiv:2605.08954v2 Announce Type: replace-cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores. A useful candidate should also be actionable: it should be reachable from known molecules through a sequence of local structural modifications, providing explicit structural references for interpreting property changes within an evolving chemical series. Existing de novo and single-molecule optimization methods do not explicitly model such reachability, espec

Key takeaways

  • arXiv:2605.08954v2 Announce Type: replace-cross Abstract: Molecular optimization in drug discovery aims to discover molecules with improved target properties, but practical lead optimization often requires more than high predicted scores.
  • A useful candidate should also be actionable: it should be reachable from known molecules through a sequence of local structural modifications, providing explicit structural references for interpreting property changes within an evolving chemical series.
  • Existing de novo and single-molecule optimization methods do not explicitly model such reachability, espec

Why it matters

This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗