Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon 16678, Republic of Korea.
J Chem Inf Model. 2021 Jan 25;61(1):123-133. doi: 10.1021/acs.jcim.0c01074. Epub 2021 Jan 7.
Retrosynthesis is an essential task in organic chemistry for identifying the synthesis pathways of newly discovered materials, and with the recent advances in deep learning, there have been growing attempts to solve the retrosynthesis problem through transformer models, which are the state-of-the-art in neural machine translation, by converting the problem into a machine translation problem. However, the pure transformer provides unsatisfactory results that lack grammatical validity, chemical plausibility, and diversity in reactant candidates. In this study, we develop tied two-way transformers with latent modeling to solve those problems using cycle consistency checks, parameter sharing, and multinomial latent variables. Experimental results obtained using public and in-house datasets demonstrate that the proposed model improves the retrosynthesis accuracy, grammatical error, and diversity, and qualitative evaluation results verify its ability to suggest valid and plausible results.
逆合成分析是有机化学中识别新发现材料合成途径的一项基本任务，随着深度学习的最新进展，越来越多的人试图通过将问题转化为机器翻译问题来使用基于转换器的模型（神经机器翻译的最新技术）来解决逆合成问题。然而，纯转换器提供的结果令人不满意，缺乏语法有效性、化学合理性和反应物候选物的多样性。在这项研究中，我们使用循环一致性检查、参数共享和多项潜在变量开发了带有潜在建模的双向连接转换器，以解决这些问题。使用公共和内部数据集获得的实验结果表明，所提出的模型提高了逆合成的准确性、语法错误和多样性，定性评估结果验证了其生成有效和合理结果的能力。
