化学与几何表示保真度提升药物-靶点亲和力预测
Chemical and geometric representation fidelity improves drug--target affinity prediction
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中文总结 AI 辅助
针对药物-靶点亲和力预测中表示阶段信息丢失的瓶颈，提出ReGeoDTA框架，通过保持化学异质性与连续几何关系提升预测性能，并在多数据集与架构中验证其有效性。
中文摘要 AI 辅助
预测药物-靶点结合亲和力（DTA）要求模型能够区分分子识别背后的细微化学和结构决定因素。尽管近期方法越来越多地整合更丰富的药物和蛋白质信息，但在表示构建过程中，这些信息可能被压缩、同质化或离散化，导致与亲和力相关的区分特征在相互作用建模之前就已丢失。我们假设这种表示阶段的信息丢失构成了上游瓶颈，而日益复杂的相互作用预测器无法可靠地克服这一瓶颈。为验证该假设，我们开发了ReGeoDTA，一种保持表示保真的框架，该框架在分子表示中维持亲和力相关的化学异质性，并在蛋白质结构中维持连续的几何关系。在三个基准数据集上，ReGeoDTA持续提升了亲和力预测性能，且所提出的表示保真策略在不同DTA架构中均保持了其优势。受控的表示退化逐步降低了预测性能，而增加下游预测器的复杂度却无法恢复表示构建过程中丢失的信息。这些发现将表示保真度确立为准确且可泛化的药物-靶点亲和力预测的上游设计原则，对计算化合物优先级排序具有潜在意义。
英文摘要
Predicting drug--target binding affinity (DTA) requires models to distinguish subtle chemical and structural determinants underlying molecular recognition. Although recent approaches increasingly incorporate richer drug and protein information, such information may be compressed, homogenized or discretized during representation construction, causing affinity-relevant distinctions to be lost before interaction modelling. We hypothesized that this representation-stage information loss constitutes an upstream bottleneck that cannot be reliably overcome by increasingly complex interaction predictors. To test this hypothesis, we developed ReGeoDTA, a representation-preserving framework that maintains affinity-relevant chemical heterogeneity in molecular representations and continuous geometric relationships in protein structures. Across three benchmark datasets, ReGeoDTA consistently improved affinity prediction, and the proposed representation-preserving strategies retained their benefits across diverse DTA architectures. Controlled representation degradation progressively reduced predictive performance, whereas increasing downstream predictor complexity failed to recover information lost during representation construction. These findings identify representation fidelity as an upstream design principle for accurate and generalizable drug--target affinity prediction, with potential implications for computational compound prioritization.
发表机构
- Northeastern University（东北大学）
- Northeastern University at Qinhuangdao（东北大学秦皇岛分校）
- Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。
