用验证框架提升大模型生成可合成分子的能力，成功率从3%升至83%
VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Design
- 通过提示优化+自动化学验证+领域微调，确保生成分子真实可合成
- 生成分子有效率从3%提升至83%，预测结合亲和力最高提升17倍
- 适合药物设计、化学生成等需高准确性的科研场景
大语言模型在科学发现中展现巨大潜力，但在需要事实精确性和专业领域约束的分子设计中仍面临挑战。在药物研发的分子设计中，这些模型虽能提出创新修改，但常生成化学上不可行的结构。我们提出VALID-Mol，一个整合化学验证与大模型驱动分子设计的综合框架，将有效化学结构生成率从3%提升至83%。该方法融合系统性提示优化、自动化化学验证和领域自适应微调，确保生成具有优良性质且可合成的分子。计算分析表明，该框架生成的候选分子在目标结合亲和力上最高可实现17倍的预测提升，同时保持合成可行性。我们的贡献不仅在于实现细节，更提供了一种可迁移的科学约束型大模型应用方法，显著提升可靠性。
原文摘要 · Abstract (English)
Large Language Models demonstrate substantial promise for advancing scientific discovery, yet their deployment in disciplines demanding factual precision and specialized domain constraints presents significant challenges. Within molecular design for pharmaceutical development, these models can propose innovative molecular modifications but frequently generate chemically infeasible structures. We introduce VALID-Mol, a comprehensive framework that integrates chemical validation with LLM-driven molecular design, achieving an improvement in valid chemical structure generation from 3% to 83%. Our methodology synthesizes systematic prompt optimization, automated chemical verification, and domain-adapted fine-tuning to ensure dependable generation of synthesizable molecules with enhanced properties. Our contribution extends beyond implementation details to provide a transferable methodology for scientifically-constrained LLM applications with measurable reliability enhancements. Computational analyses indicate our framework generates promising synthesis candidates with up to 17-fold predicted improvements in target binding affinity while preserving synthetic feasibility.
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