Abstract Computer-aided molecular design requires the ability to predict different molecular properties of interesting from using molecular structure. Traditional quantitative structural property relations were developed by extracting molecular features for predicting various properties. Hence domains of molecular features are different for predictions of different properties. In this work, the concept of a universal translator was used to develop a universal digital chemical space by translating and projecting the chemical representation SMILES to a high-dimensional space that can be collapsed into different molecular fingerprints. We demonstrated different kinds of pure component properties, such as electrical and thermodynamic properties can be predicted by a simple input of molecular structure, SMILES. This method eliminates the need to manually extract different molecular features for predicting different properties. The ability of model to predict sigma profiles also pave the way of prediction phase equilibria of mixtures using molecular structure only.

中文翻译：

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迈向纯组分特性预测的通用数字化学空间

摘要 计算机辅助分子设计需要能够通过使用分子结构来预测感兴趣的不同分子特性。传统的定量结构性质关系是通过提取分子特征来预测各种性质的。因此，对于不同性质的预测，分子特征的域是不同的。在这项工作中，通用翻译器的概念被用于通过将化学表示 SMILES 翻译和投影到可以折叠成不同分子指纹的高维空间来开发通用数字化学空间。我们展示了不同种类的纯组分特性，例如可以通过简单输入分子结构 SMILES 来预测电和热力学特性。这种方法无需手动提取不同的分子特征来预测不同的特性。模型预测 sigma 曲线的能力也为仅使用分子结构预测混合物的相平衡铺平了道路。