Graph neural networks (GNNs) have emerged as powerful tools to accurately
predict materials and molecular properties in computational discovery
pipelines. In this article, we exploit the invertible nature of these neural
networks to directly generate molecular structures with desired electronic
properties. Starting from a random graph or an existing molecule, we perform a
gradient ascent while holding the GNN weights fixed in order to optimize its
input, the molecular graph, towards the target property. Valence rules are
enforced strictly through a judicious graph construction. The method relies
entirely on the property predictor; no additional training is required on
molecular structures. We demonstrate the application of this method by
generating molecules with specific DFT-verified energy gaps and octanol-water
partition coefficients (logP). Our approach hits target properties with rates
comparable to or better than state-of-the-art generative models while
consistently generating more diverse molecules.

利用可逆神经网络直接生成具有期望电子性质的分子结构，该方法不需要对分子结构进行额外训练，通过严格的图构建规则确保化合价规则，将该方法应用于生成具有特定能隙和分配系数（logP）的分子，并在生成更多样化分子的同时，以与最先进的生成模型相媲美甚至更好的速率实现目标性质。

使用 GNN 特性预测器作为分子生成器

Using GNN property predictors as molecule generators

Generative models in molecular design tend to be richly parameterized,
data-hungry neural models, as they must create complex structured objects as
outputs. Estimating such models from data may be challenging due to the lack of
sufficient training data. In this paper, we propose a surprisingly effective
self-training approach for iteratively creating additional molecular targets.
We first pre-train the generative model together with a simple property
predictor. The property predictor is then used as a likelihood model for
filtering candidate structures from the generative model. Additional targets
are iteratively produced and used in the course of stochastic EM iterations to
maximize the log-likelihood that the candidate structures are accepted. A
simple rejection (re-weighting) sampler suffices to draw posterior samples
since the generative model is already reasonable after pre-training. We
demonstrate significant gains over strong baselines for both unconditional and
conditional molecular design. In particular, our approach outperforms the
previous state-of-the-art in conditional molecular design by over 10% in
absolute gain. Finally, we show that our approach is useful in other domains as
well, such as program synthesis.

本文提出了一种自我训练的迭代方法，该方法利用简单的属性预测模型来过滤候选结构，提高生成模型的预测效果，并在无条件和条件下的分子设计中均获得了显著的性能优势。