用基因表达谱指导药物分子设计，实现精准可控的结构优化。
PhenoMoler: Phenotype-Guided Molecular Optimization via Chemistry Large Language Model
- 基于化学大模型与转录组数据联合生成分子
- 生成分子在药效与理化性质上优于或媲美已批准药物
- 支持对骨架、侧链等结构单元的精细调控
当前分子生成模型多关注药物-靶点结合亲和力与特异性，常忽略化合物引发的整体表型效应。转录谱作为药物诱导表型变化的分子级读数，为表型感知的分子设计提供了契机。我们提出PhenoMoler，一种整合化学大语言模型与表达谱的表型引导分子生成框架，通过条件化于药物诱导的差异表达信号，显式建立转录响应与化学结构的关联。通过选择性掩码与重建特定子结构（如骨架、侧链或连接基），实现细粒度、可控制的分子优化。大量实验表明，PhenoMoler生成的分子在化学有效性、新颖性与多样性上均表现优异，且与目标表型一致。相比已获批药物，生成化合物具有相当或更优的类药性（QED）、优化的理化性质，以及对关键癌症靶点更强的结合亲和力。结果凸显了PhenoMoler在表型引导与结构可控分子优化中的潜力。
原文摘要 · Abstract (English)
Current molecular generative models primarily focus on improving drug-target binding affinity and specificity, often neglecting the system-level phenotypic effects elicited by compounds. Transcriptional profiles, as molecule-level readouts of drug-induced phenotypic shifts, offer a powerful opportunity to guide molecular design in a phenotype-aware manner. We present PhenoMoler, a phenotype-guided molecular generation framework that integrates a chemistry large language model with expression profiles to enable biologically informed drug design. By conditioning the generation on drug-induced differential expression signatures, PhenoMoler explicitly links transcriptional responses to chemical structure. By selectively masking and reconstructing specific substructures-scaffolds, side chains, or linkers-PhenoMoler supports fine-grained, controllable molecular optimization. Extensive experiments demonstrate that PhenoMoler generates chemically valid, novel, and diverse molecules aligned with desired phenotypic profiles. Compared to FDA-approved drugs, the generated compounds exhibit comparable or enhanced drug-likeness (QED), optimized physicochemical properties, and superior binding affinity to key cancer targets. These findings highlight PhenoMoler's potential for phenotype-guided and structure-controllable molecular optimization.
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