Solubility prediction is a standard benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.e. the aleatoric limit) are not yet reliable enough to be deployed. We argue that this gap is partly artefactual: published benchmarks differ in curation policies, evaluate on count-weighted RMSE that hides failure on tail-heavy solvent distributions, and treat the widely cited 0.6-0.8 log S inter-laboratory figure as the aleatoric ceiling even though it reflects worst-case, not expected, disagreement. We introduce SC3, a multi-solvent solubility benchmark built on BigSolDB v2.1 with three contributions: (i) a reproducible curation pipeline yielding 101,535 measurements over 1,327 solutes and 206 solvents, with a recalibrated aleatoric floor of 0.106 log S-roughly 6 times tighter than the conventional figure; (ii) nested Gold/Silver/Bronze consensus tiers with per-point standard deviation, three leakage-checked splits, and a multi-solvent metric suite (PS-RMSE, Z-RMSE); and (iii) a 31-model benchmark across six families, whose best Bronze PS-RMSE sits at 5 times the aleatoric limit, and we observe this is a gap unclosed by any deep alternative tested. We perform three follow-on analyses: data scaling, transfer from quantum-chemistry solvation energies, and feature-level attribution, which demonstrates that calibrated per-point uncertainty is a reusable infrastructure for diagnosis beyond point prediction.

翻译：溶解度预测是计算化学中的标准基准任务，然而，据称接近实验噪声上限（即随机极限）的多溶剂模型尚不足以可靠部署。我们认为这一差距部分源于人为因素：已发表的基准在数据筛选策略上存在差异，基于计数加权均方根误差的评估掩盖了在尾部厚重的溶剂分布上的失效，并且将广泛引用的0.6-0.8 log S实验室间差异视为随机极限，尽管这反映的是最坏情况而非预期差异。我们引入SC3，这是一个基于BigSolDB v2.1构建的多溶剂溶解度基准，其贡献包括三方面：（i）一个可复现的数据筛选流程，产出包含101,535个测量值、涵盖1,327种溶质和206种溶剂的数据集，并将校准后的随机下限设为0.106 log S——约为传统数值的六分之一；（ii）嵌套的金/银/铜共识层级（含逐点标准差）、三种泄漏检查划分方法以及一套多溶剂评估指标（PS-RMSE、Z-RMSE）；（iii）涵盖六个家族的31个模型基准，其中最佳铜级PS-RMSE仍为随机极限的五倍，我们观察到这一差距未被任何测试的深度替代方案所弥合。我们执行了三个后续分析：数据规模缩放、从量子化学溶剂化能量进行迁移学习以及特征层面归因，结果表明校准后的逐点不确定性是一种可复用的基础设施，适用于超越点预测的诊断。