TSSR Improves SMILES Generation Validity and Novelty
Researchers present TSSR, a two-stage, swap-reward-driven reinforcement learning framework for character-level SMILES generation, introduced in a preprint submitted Jan 8, 2026. Stage one rewards local token swaps to repair syntax; stage two gives RDKit-based chemistry diagnostics to reduce valence, aromaticity, and connectivity errors. Evaluated on MOSES with GRU+PPO in pure and fine-tuning RL, TSSR increases syntactic and chemical validity and novelty while preserving drug-likeness and diversity.
Scoring Rationale
Model-agnostic two-stage reward design improves validity and novelty; limited impact outside SMILES-based molecular generation applications.
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