Phan Tieu-Long, Weinbauer Klaus, Gärtner Thomas, Merkle Daniel, Andersen Jakob L, Fagerberg Rolf, Stadler Peter F
Bioinformatics Group, Department of Computer Science and Interdisciplinary Center for Bioinformatics and School for Embedded and Composite Artificial Intelligence (SECAI), Leipzig University, Härtelstraße 16-18, 04107, Leipzig, Germany.
Department of Mathematics and Computer Science, University of Southern Denmark, 5230, Odense M, Denmark.
J Cheminform. 2024 Jul 19;16(1):82. doi: 10.1186/s13321-024-00875-4.
Reaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.
The SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.
The rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.
The SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.
SynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.
反应数据库是计算化学和生物化学中各种应用的关键资源，包括计算机辅助合成规划（CASP）和代谢网络的大规模分析。只有数据集准确完整，这些资源的全部潜力才能得以实现。然而，缺少共反应物和共产物，即反应不平衡，却是普遍现象而非个别情况。因此，对这些不完整条目进行整理和修正迫在眉睫。
SynRBL框架采用双重策略解决此问题：一种针对非碳化合物的基于规则的方法，利用原子符号和计数进行预测，同时采用一种基于最大公共子图（MCS）的技术处理碳化合物，旨在对齐反应物和产物以推断缺失的实体。
基于规则的方法准确率超过99%，而基于MCS的准确率则因反应性质而异，在81.19%至99.33%之间。此外，还设计了一个适用域和一个机器学习评分函数来量化预测置信度。该框架的整体效能通过成功率和准确率指标来描述，成功率分别为89.83%至99.75%，准确率为90.85%至99.05%。
SynRBL框架为重新校准化学反应提供了一种新颖的解决方案，显著提高了反应的完整性。经过严格验证，它在反应重新平衡方面取得了突破性的准确率。这为未来特别是原子到原子映射技术以及诸如自动合成规划等下游任务的改进奠定了基础。
SynRBL具有一种新颖的计算方法，用于校正化学反应数据库中的不平衡条目。通过结合推断非碳化合物的启发式规则和用于解决碳不平衡的公共子图搜索，SynRBL成功解决了这个问题的大多数实例，而这个问题影响了大多数大规模资源中的大部分数据。与其他替代解决方案相比，SynRBL在成功率和准确率方面都有显著提高，并为这个问题提供了首个免费的开源解决方案。
