ChemCLIP：通过对比学习弥合有机和无机抗癌化合物的鸿沟
ChemCLIP: Bridging Organic and Inorganic Anticancer Compounds Through Contrastive Learning
AI总结：
本文提出ChemCLIP框架，通过对比学习统一有机和无机抗癌化合物的表示，利用共享抗癌活性而非结构相似性，提升跨领域化学知识迁移能力。
摘要：
抗癌药物的发现传统上将有机小分子和金属基配位复杂物视为不同化学领域，尽管它们有共同的生物学目标，但现有数据的不均衡限制了知识迁移。本文引入ChemCLIP，一种双编码器对比学习框架，通过学习基于共享抗癌活性的统一表示来弥合有机-无机鸿沟。我们编译了包含44,854种独特有机化合物和5,164种独特金属复杂物的互补数据集，标准化于60种癌症细胞系。通过训练具有活动意识的硬负样本挖掘的并行编码器，我们将结构上不同的化合物映射到共享的256维嵌入空间中，其中生物上相似的化合物无论化学类别如何都聚类在一起。我们通过定量对齐度量、嵌入可视化和下游分类任务系统评估了四种分子编码策略：Morgan指纹、ChemBERTa、MolFormer和Chemprop。Morgan指纹在平均对齐度为0.899的情况下，下游分类AUCs分别为0.859（无机）和0.817（有机）。本工作确立了对比学习在统一不同化学领域中的有效性，并为多模式化学应用中的编码器选择提供了实证指导，其影响不仅限于抗癌药物发现，还扩展到任何需要跨领域化学知识迁移的场景。
英文摘要：
The discovery of anticancer therapeutics has traditionally treated organic small molecules and metal-based coordination complexes as separate chemical domains, limiting knowledge transfer despite their shared biological objectives. This disparity is particularly pronounced in available data, with extensive screening databases for organic compounds compared to only a few thousand characterized metal complexes. Here, we introduce ChemCLIP, a dual-encoder contrastive learning framework that bridges this organic-inorganic divide by learning unified representations based on shared anticancer activities rather than structural similarity. We compiled complementary datasets comprising 44,854 unique organic compounds and 5,164 unique metal complexes, standardized across 60 cancer cell lines. By training parallel encoders with activity-aware hard negative mining, we mapped structurally distinct compounds into a shared 256-dimensional embedding space where biologically similar compounds cluster together regardless of chemical class. We systematically evaluated four molecular encoding strategies: Morgan fingerprints, ChemBERTa, MolFormer, and Chemprop, through quantitative alignment metrics, embedding visualizations, and downstream classification tasks. Morgan fingerprints achieved superior performance with an average alignment ratio of 0.899 and downstream classification AUCs of 0.859 (inorganic) and 0.817 (organic). This work establishes contrastive learning as an effective strategy for unifying disparate chemical domains and provides empirical guidance for encoder selection in multi-modal chemistry applications, with implications extending beyond anticancer drug discovery to any scenario requiring cross-domain chemical knowledge transfer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
