Analog Accessibility Score (AAscore) for Rational Compound Selection.

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-12-23 Epub Date: 2024-12-06 DOI:10.1021/acs.jcim.4c01691
Takato Ue, Akinori Sato, Tomoyuki Miyao
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Abstract

Various in silico 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.

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合理化合物选择的模拟可及性评分(AAscore)。
已经提出了各种硅分数来客观地评价化合物的特征和性质。然而,仍然没有分数来表示一个化合物的模拟可及性。这样的分数对于选择通过虚拟筛选提出的化合物或优先考虑hit-to-lead阶段的hit化合物是有价值的。本研究提出了一个模拟物可及性评分(AAscore),其中利用反向合成预测和正向产品预测模型来生成虚拟模拟物。AAscore定义为唯一类似物和虚拟合成路由的数量。为了从实际合成类似物的数量来评价AAscore,采用化合物核关系(CCR)法制备类似物。结果发现,AAscore与基于ccr的类似物的数量相关性不大。此外,从反应物数据库中提取的候选反应物数量对AAscores有显著影响。一个针对碳酸酐酶2活性化合物的案例研究表明,AAscore可以识别合成类似物的化合物。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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