AI 中文总结
RAVEN模型采用冻结原子图编码器生成多视图结构投影，结合物理化学交互指纹与异构回归器融合，在PDBbind和CASF数据集上实现了蛋白质-配体结合亲和力的稳健预测。
AI 中文摘要
从三维复合物结构定量估计蛋白质-配体结合亲和力是基于结构的计算化学和分子建模中的基础任务。可靠的预测仍具挑战性，因为可用的结构-亲和力数据有限、实验异质、依赖构象且对数据集划分敏感。RAVEN（Randomized Atomistic Views with Ensemble Neural Reservoirs，即带集成神经储备池的随机原子视图）利用多头储备池，该储备池包含独立初始化且完全冻结的原子图编码器，以生成多样化的结构投影，无需对图表示进行端到端优化。这些投影与确定性的物理化学交互指纹相结合，由异构监督读取器处理，包括神经回归器和基于树的回归器，其输出通过基于验证的非负融合进行组合。随机储备池扩展了独立编码器实现的结构特征覆盖范围，而显式物理化学描述符和异构读取器则提供互补信息和不同的归纳偏置。在使用GEMS相似性资源重构的相似性隔离PDBbind 2020R1划分以及受保护的CASF-2016子集上进行的评估显示出强大的预测性能。结果表明，冻结的多视图图表示、显式物理化学统计以及异构模型融合为蛋白质-配体结合亲和力预测提供了一种稳健且灵活的框架。
英文摘要
Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling. Reliable prediction remains challenging because available structure-affinity data are limited, experimentally heterogeneous, conformation-dependent, and sensitive to dataset partitioning. RAVEN (Randomized Atomistic Views with Ensemble Neural Reservoirs) utilizes a multihead reservoir of independently initialized and fully frozen atomistic graph encoders to generate diverse structural projections without end-to-end optimization of the graph representation. These projections are integrated with a deterministic physicochemical interaction fingerprint and processed by heterogeneous supervised readers, including neural and tree-based regressors, whose outputs are combined through validation-based nonnegative fusion. The random reservoir expands structural feature coverage across independent encoder realizations, whereas the explicit physicochemical descriptors and heterogeneous readers contribute complementary information and distinct inductive biases. Evaluation on a similarity-isolated PDBbind 2020R1 split reconstructed using GEMS similarity resources, together with the protected CASF-2016 subset, demonstrated strong predictive performance. The results indicate that frozen multi-view graph representations, explicit physicochemical statistics, and heterogeneous model fusion provide a robust and flexible framework for protein-ligand binding-affinity prediction.
