Abstract：
In a unique collaboration between Simulations Plus and several industrial partners, we were able to develop a new version 11.0 of the previously published in silico pKa model, S+pKa, with considerably improved prediction accuracy. The model\'s training set was vastly expanded by large amounts of experimental data obtained from F. Hoffmann-La Roche AG, Genentech Inc., and the Crop Science division of Bayer AG. The previous v7.0 of S+pKa was trained on data from public sources and the Pharmaceutical division of Bayer AG. The model has shown dramatic improvements in predictive accuracy when externally validated on three new contributor compound sets. Less expected was v11.0\'s improvement in prediction on new compounds developed at Bayer Pharma after v7.0 was released (2013-2023), even without contributing additional data to v11.0. We illustrate chemical space coverage by chemistries encountered in the five domains, public and industrial, outline model construction, and discuss factors contributing to model\'s success.
摘要：
在SimulationsPlus和几个工业合作伙伴之间的独特合作中，我们能够开发以前发布的pKa模型的新版本11.0，S+pKa,大大提高了预测精度。从F.Hoffmann-LaRocheAG获得的大量实验数据极大地扩展了模型的训练集，GenentechInc.,和拜耳公司的作物科学部门。以前的S+pKa的v7.0是根据来自公共来源和拜耳公司制药部门的数据进行培训的。当在三个新的贡献者化合物集上进行外部验证时，该模型在预测准确性方面显示出了显着提高。预期较低的是v11.0在发布v7.0后（2013-2023年），对拜耳制药公司开发的新化合物的预测有所改善。即使没有为v11.0贡献额外的数据。我们通过在五个领域中遇到的化学物质来说明化学空间覆盖，公共和工业，大纲模型构建，并讨论了模型成功的因素。
