标题： ToxBench：一个具有AB-FEP计算标签的人类雌激素受体α结合亲和力预测基准 显示英文标题

标题： ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha

摘要： 蛋白质-配体结合亲和力预测对于药物发现和毒性评估至关重要。 虽然机器学习（ML）有望实现快速且准确的预测，但其进展受到可靠数据可用性的限制。 相比之下，基于物理的方法，如绝对结合自由能扰动（AB-FEP），能够提供高精度，但对于高通量应用来说计算成本过高。 为了弥合这一差距，我们引入了ToxBench，这是首个针对机器学习开发的大规模AB-FEP数据集，专注于单一药学关键靶点，即人雌激素受体α（ER$\alpha$）。 ToxBench包含8,770个ER$\alpha$-配体复合结构，其结合自由能通过AB-FEP计算，并且子集已通过实验亲和力验证，RMSE为1.75 kcal/mol，同时还包括非重叠的配体分割以评估模型的泛化能力。 使用ToxBench，我们进一步对最先进的机器学习方法进行了基准测试，值得注意的是，我们提出的DualBind模型，该模型采用双损失框架来有效学习结合能量函数。 基准结果表明，DualBind表现出优越的性能，且机器学习在计算成本大幅降低的情况下具有近似AB-FEP的潜力。 显示英文摘要

摘要： Protein-ligand binding affinity prediction is essential for drug discovery and toxicity assessment. While machine learning (ML) promises fast and accurate predictions, its progress is constrained by the availability of reliable data. In contrast, physics-based methods such as absolute binding free energy perturbation (AB-FEP) deliver high accuracy but are computationally prohibitive for high-throughput applications. To bridge this gap, we introduce ToxBench, the first large-scale AB-FEP dataset designed for ML development and focused on a single pharmaceutically critical target, Human Estrogen Receptor Alpha (ER$\alpha$). ToxBench contains 8,770 ER$\alpha$-ligand complex structures with binding free energies computed via AB-FEP with a subset validated against experimental affinities at 1.75 kcal/mol RMSE, along with non-overlapping ligand splits to assess model generalizability. Using ToxBench, we further benchmark state-of-the-art ML methods, and notably, our proposed DualBind model, which employs a dual-loss framework to effectively learn the binding energy function. The benchmark results demonstrate the superior performance of DualBind and the potential of ML to approximate AB-FEP at a fraction of the computational cost.