ArticleFindings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing2025
Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization.
Article in Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
In real-world drug design, molecule optimization requires selectively improving multiple molecular properties up to pharmaceutically relevant levels, while maintaining others that already meet such criteria. However, existing computational approaches and instruction-tuned LLMs fail to capture such nuanced property-specific objectives, limiting their practical applicability. To address this, we introduce C-MuMOInstruct, the first instruction-tuning dataset focused on multi-property optimization with explicit, property-specific objectives. Leveraging C-MuMOInstruct, we develop GeLLM
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Registered trials
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