Ue Takato, Sato Akinori, Miyao Tomoyuki
Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara630-0192, Japan.
Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara630-0192, Japan.
J Chem Inf Model. 2024 Dec 23;64(24):9350-9360. doi: 10.1021/acs.jcim.4c01691. Epub 2024 Dec 6.
Various scores have been proposed to objectively assess the characteristics and properties of a compound. However, there is still no score that represents the analog accessibility of a compound. Such a score would be valuable for selecting compounds proposed by virtual screening or for prioritizing hit compounds for the hit-to-lead phase. This study proposes an analog accessibility score (AAscore), where retrosynthesis prediction and forward product prediction models were utilized to generate virtual analogs. The AAscore is defined as the number of unique analogs and virtual synthetic routes. To evaluate the AAscore in terms of the number of actually synthesized analog compounds, analog compounds were prepared by using the compound-core relationship (CCR) method. It was found that the AAscore was little correlated with the number of CCR-based analogs. Furthermore, AAscores were found to be significantly influenced by the number of extracted candidate reactants from a reactant database. A case study targeting compounds active against carbonic anhydrase 2 showed that the AAscore could identify compounds that were synthesized into analogs.
已经提出了各种分数来客观评估化合物的特征和性质。然而，仍然没有一个分数能够代表化合物的类似物可及性。这样的分数对于选择虚拟筛选提出的化合物或在从 hits 化合物到先导化合物阶段对 hits 化合物进行优先级排序将是有价值的。本研究提出了一种类似物可及性分数（AAscore），其中利用逆合成预测和正向产物预测模型来生成虚拟类似物。AAscore 被定义为独特类似物和虚拟合成路线的数量。为了根据实际合成的类似物化合物的数量来评估 AAscore，使用化合物 - 核心关系（CCR）方法制备了类似物化合物。发现 AAscore 与基于 CCR 的类似物的数量几乎没有相关性。此外，发现 AAscore 受到从反应物数据库中提取的候选反应物数量的显著影响。一项针对对碳酸酐酶 2 有活性的化合物的案例研究表明，AAscore 可以识别出能够合成类似物的化合物。
