加速基于字符串生成的化学反应模型推理以用于工业应用
Accelerating the inference of string generation-based chemical reaction models for industrial applications
AI总结：
针对无模板SMILES翻译模型推理慢的问题，提出基于投机解码的加速方法，在分子变换器上实现超3倍加速且精度不变。
摘要：
用于反应预测和单步逆合成的无模板SMILES到SMILES翻译模型，因其最先进的准确性而在计算机辅助合成规划系统的工业应用中备受关注。然而，它们存在推理速度慢的问题。我们提出了一种通过投机解码来加速自回归SMILES生成器推理的方法，该方法将查询字符串子序列复制到目标字符串中的正确位置。我们将该方法应用于在Pytorch Lightning中实现的分子变换器，在反应预测和单步逆合成中实现了超过3倍的推理加速，且准确性无损失。
英文摘要：
Template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis are of interest for industrial applications in computer-aided synthesis planning systems due to their state-of-the-art accuracy. However, they suffer from slow inference speed. We present a method to accelerate inference in autoregressive SMILES generators through speculative decoding by copying query string subsequences into target strings in the right places. We apply our method to the molecular transformer implemented in Pytorch Lightning and achieve over 3X faster inference in reaction prediction and single-step retrosynthesis, with no loss in accuracy.
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