SYBA (SYnthetic Bayesian Accessibility) is a fragment-based method for the rapid classification of organic compounds as easy- (ES) or hard-to-synthesize (HS). It is based on a Bernoulli naïve Bayes classifier that is used to assign SYBA score contributions to individual fragments based on their frequencies in the database of ES and HS molecules. SYBA was trained on ES molecules available in the ZINC15 database and on HS molecules generated by the Nonpher methodology. SYBA was compared with a random forest, that was utilized as a baseline method, as well as with other two methods for synthetic accessibility assessment: SAScore and SCScore. When used with their suggested thresholds, SYBA improves over random forest classification, albeit marginally, and outperforms SAScore and SCScore. However, upon the optimization of SAScore threshold (that changes from 6.0 to – 4.5), SAScore yields similar results as SYBA. Because SYBA is based merely on fragment contributions, it can be used for the analysis of the contribution of individual molecular parts to compound synthetic accessibility. SYBA is publicly available at https://github.com/lich-uct/syba under the GNU General Public License.

中文翻译：

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SYBA：有机化合物合成可及性的贝叶斯估计


SYBA（合成贝叶斯可访问性）是一种基于片段的方法，用于将有机化合物快速分类为易合成（ES）或难合成（HS）。它基于伯努利朴素贝叶斯分类器，用于根据 ES 和 HS 分子数据库中的频率将 SYBA 分数贡献分配给各个片段。 SYBA 接受了 ZINC15 数据库中可用的 ES 分子和 Nonpher 方法生成的 HS 分子的培训。将 SYBA 与用作基线方法的随机森林以及其他两种综合可达性评估方法：SAScore 和 SCScore 进行比较。当与建议的阈值一起使用时，SYBA 比随机森林分类有所改进（尽管幅度有限），并且优于 SAScore 和 SCScore。然而，在优化 SAScore 阈值（从 6.0 变为 – 4.5）后，SAScore 产生与 SYBA 相似的结果。由于 SYBA 仅基于片段贡献，因此它可用于分析单个分子部分对化合物合成可及性的贡献。 SYBA 根据 GNU 通用公共许可证在 https://github.com/lich-uct/syba 上公开提供。