The problem of accelerating drug discovery relies heavily on automatic tools
to optimize precursor molecules to afford them with better biochemical
properties. Our work in this paper substantially extends prior state-of-the-art
on graph-to-graph translation methods for molecular optimization. In
particular, we realize coherent multi-resolution representations by
interweaving the encoding of substructure components with the atom-level
encoding of the original molecular graph. Moreover, our graph decoder is fully
autoregressive, and interleaves each step of adding a new substructure with the
process of resolving its attachment to the emerging molecule. We evaluate our
model on multiple molecular optimization tasks and show that our model
significantly outperforms previous state-of-the-art baselines.

本文重点研究了药物发现方面的自动优化工具，并且提出了一种基于图像及其翻译方法的分子优化技术，利用其原子级编码和自回归图形解码器解决分子结构中的多个技术挑战。同时，我们的模型在多项任务中显著优于现有的基准模型。

分层图到图分子翻译

Hierarchical Graph-to-Graph Translation for Molecules

We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph. A key challenge is therefore to model diverse translation
outputs. Our primary contributions include a junction tree encoder-decoder for
learning diverse graph translations along with a novel adversarial training
method for aligning distributions of molecules. Diverse output distributions in
our model are explicitly realized by low-dimensional latent vectors that
modulate the translation process. We evaluate our model on multiple molecular
optimization tasks and show that our model outperforms previous
state-of-the-art baselines.

本文采用图表征相似的分子并通过图表征将其转换为拥有更好性质的分子，采用基于树的模型以支持多样性的输出，并用低维潜在向量扭曲转换过程以实现多样性，并且用对抗训练方法以对齐分子的分布。实验表明，该模型优于目前最先进的基准。