将电子云信号量化为可学习的令牌，提升蛋白质-配体结合预测精度
Tokenizing Electron Cloud in Protein-Ligand Interaction Learning
- 用结构感知的Transformer与分层码本对电子云进行去冗余编码
- 在结合亲和力预测任务中，皮尔逊相关系数提升6.42%，斯皮尔曼相关系数提升15.58%
- 适合关注电子结构作用的药物发现研究者，尤其适用于复杂相互作用建模
蛋白质-分子结合的亲和力与特异性直接影响功能结果，揭示生物调控与信号转导机制。现有深度学习方法多聚焦原子或片段结构，而量子化学特性如电子结构是揭示相互作用模式的关键，却未被充分探索。为此，我们提出ECBind，将电子云信号转化为量化嵌入表示，可集成至下游任务如结合亲和力预测。通过引入电子密度，ECBind能捕捉原子级模型无法完全表征的结合模式。具体而言，采用结构感知Transformer与分层码本，对富含电子结构信息的3D结合位点进行编码。这些编码令牌用于有标签任务。为扩大适用性，采用知识蒸馏构建无需依赖电子云的预测模型。实验表明，ECBind在多个任务中达到当前最优性能，每结构皮尔逊相关系数提升6.42%，斯皮尔曼相关系数提升15.58%。
原文摘要 · Abstract (English)
The affinity and specificity of protein-molecule binding directly impact functional outcomes, uncovering the mechanisms underlying biological regulation and signal transduction. Most deep-learning-based prediction approaches focus on structures of atoms or fragments. However, quantum chemical properties, such as electronic structures, are the key to unveiling interaction patterns but remain largely underexplored. To bridge this gap, we propose ECBind, a method for tokenizing electron cloud signals into quantized embeddings, enabling their integration into downstream tasks such as binding affinity prediction. By incorporating electron densities, ECBind helps uncover binding modes that cannot be fully represented by atom-level models. Specifically, to remove the redundancy inherent in electron cloud signals, a structure-aware transformer and hierarchical codebooks encode 3D binding sites enriched with electron structures into tokens. These tokenized codes are then used for specific tasks with labels. To extend its applicability to a wider range of scenarios, we utilize knowledge distillation to develop an electron-cloud-agnostic prediction model. Experimentally, ECBind demonstrates state-of-the-art performance across multiple tasks, achieving improvements of 6.42\% and 15.58\% in per-structure Pearson and Spearman correlation coefficients, respectively.
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