提出新方法评估分子可合成性，解决生成药物分子难合成问题。
Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction
- 结合逆合成规划与反应预测，构建数据驱动的可合成性评估指标。
- 在多种生成模型上验证，发现传统评分无法保证实际合成路径存在。
- 适合药物生成、分子设计领域研究者使用，尤其关注可合成性问题。
当前药物设计生成模型面临药理性质与可合成性之间的权衡挑战：预测具有良好性质的分子往往难以合成，而易合成的分子性质又不够理想。因此，在一般药物设计场景中评估分子可合成性仍是重大难题。常用的合成可及性（SA）分数虽能评估合成难易程度，但无法确保实际合成路径的存在。受顶层合成路径生成和正向反应预测进展的启发，我们提出一种新的、基于数据驱动的分子可合成性评估指标。该指标充分利用逆合成规划器与反应预测器之间的协同双重性，二者均基于大规模反应数据集训练。为验证该指标的有效性，我们在一系列代表性分子生成模型上进行了全面的往返评分评估。
原文摘要 · Abstract (English)
A significant challenge in wet lab experiments with current drug design generative models is the trade-off between pharmacological properties and synthesizability. Molecules predicted to have highly desirable properties are often difficult to synthesize, while those that are easily synthesizable tend to exhibit less favorable properties. As a result, evaluating the synthesizability of molecules in general drug design scenarios remains a significant challenge in the field of drug discovery. The commonly used synthetic accessibility (SA) score aims to evaluate the ease of synthesizing generated molecules, but it falls short of guaranteeing that synthetic routes can actually be found. Inspired by recent advances in top-down synthetic route generation and forward reaction prediction, we propose a new, data-driven metric to evaluate molecule synthesizability. This novel metric leverages the synergistic duality between retrosynthetic planners and reaction predictors, both of which are trained on extensive reaction datasets. To demonstrate the efficacy of our metric, we conduct a comprehensive evaluation of round-trip scores across a range of representative molecule generative models.
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