Scaffold-Conditioned Preference Triplets for Controllable Molecular Optimization with Large Language Models
The paper introduces Scaffold-Conditioned Preference Triplets (SCPT), a novel pipeline that trains large language models (LLMs) to perform molecular optimization. SCPT enables LLMs to make property-improving edits to molecules while preserving their core structural scaffold, a crucial aspect of drug discovery. The method significantly outperforms non-LLM baselines in scaffold preservation and demonstrates strong compositional generalization to unseen multi-property optimization tasks.