Mapping out reaction pathways and their corresponding activation barriers is
a significant aspect of molecular simulation. Given their inherent complexity
and nonlinearity, even generating a initial guess of these paths remains a
challenging problem. Presented in this paper is an innovative approach that
utilizes neural networks to generate initial guess for these reaction pathways.
The proposed method is initiated by inputting the coordinates of the initial
state, followed by progressive alterations to its structure. This iterative
process culminates in the generation of the approximate representation of the
reaction path and the coordinates of the final state. The application of this
method extends to complex reaction pathways illustrated by organic reactions.
Training was executed on the Transition1x dataset, an organic reaction pathway
dataset. The results revealed generation of reactions that bore substantial
similarities with the corresponding test data. The method's flexibility allows
for reactions to be generated either to conform to predetermined conditions or
in a randomized manner.

基于神经网络的方法通过迭代过程，根据初始状态的坐标生成了反应路径的近似表示和最终状态的坐标，适用于复杂的有机反应路径并能根据预设条件或随机方式生成反应。

从初始到最终状态的反应路径构建的生成模型

Generative Model for Constructing Reaction Path from Initial to Final  States

Organic reactions are usually assigned to classes containing reactions with
similar reagents and mechanisms. Reaction classes facilitate the communication
of complex concepts and efficient navigation through chemical reaction space.
However, the classification process is a tedious task. It requires the
identification of the corresponding reaction class template via annotation of
the number of molecules in the reactions, the reaction center, and the
distinction between reactants and reagents. This work shows that
transformer-based models can infer reaction classes from non-annotated, simple
text-based representations of chemical reactions. Our best model reaches a
classification accuracy of 98.2%. We also show that the learned representations
can be used as reaction fingerprints that capture fine-grained differences
between reaction classes better than traditional reaction fingerprints. The
insights into chemical reaction space enabled by our learned fingerprints are
illustrated by an interactive reaction atlas providing visual clustering and
similarity searching.

通过使用基于变换器的模型，可以从不带注释的简单文本化学反应表示中推断出反应类别，并可用作反应指纹，其分类准确率达到了 98.2%，而且较之传统反应指纹更好地捕捉反应类别之间的差异。