In this work, we introduce a method to fine-tune a Transformer-based
generative model for molecular de novo design. Leveraging the superior sequence
learning capacity of Transformers over Recurrent Neural Networks (RNNs), our
model can generate molecular structures with desired properties effectively. In
contrast to the traditional RNN-based models, our proposed method exhibits
superior performance in generating compounds predicted to be active against
various biological targets, capturing long-term dependencies in the molecular
structure sequence. The model's efficacy is demonstrated across numerous tasks,
including generating analogues to a query structure and producing compounds
with particular attributes, outperforming the baseline RNN-based methods. Our
approach can be used for scaffold hopping, library expansion starting from a
single molecule, and generating compounds with high predicted activity against
biological targets.

我们介绍了一种用于分子全新设计的 Transformer 基准生成模型的微调方法，利用 Transformer 相比循环神经网络 (RNNs) 的优越序列学习能力，我们的模型可以有效地生成具有期望属性的分子结构。与传统的基于 RNN 的模型相比，我们提出的方法在生成被预测为对多种生物目标活性化合物方面表现出优秀性能，捕捉分子结构序列中的长期依赖关系。通过生成查询结构的类似物和产生具有特定属性的化合物等多项任务的验证，我们的方法在性能上优于基准的基于 RNN 的方法。我们的方法可用于脱轴融合 (scaffold hopping)，从单个分子开始的库扩展，以及生成对生物目标具有高预测活性的化合物。

基于 Transformer 的强化学习的分子全新设计

Molecular De Novo Design through Transformer-based Reinforcement  Learning

Molecular de novo design is a critical yet challenging task in scientific
fields, aiming to design novel molecular structures with desired property
profiles. Significant progress has been made by resorting to generative models
for graphs. However, limited attention is paid to hierarchical generative
models, which can exploit the inherent hierarchical structure (with rich
semantic information) of the molecular graphs and generate complex molecules of
larger size that we shall demonstrate to be difficult for most existing models.
The primary challenge to hierarchical generation is the non-differentiable
issue caused by the generation of intermediate discrete coarsened graph
structures. To sidestep this issue, we cast the tricky hierarchical generation
problem over discrete spaces as the reverse process of hierarchical
representation learning and propose MolHF, a new hierarchical flow-based model
that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF
first generates bonds through a multi-scale architecture, then generates atoms
based on the coarsened graph structure at each scale. We demonstrate that MolHF
achieves state-of-the-art performance in random generation and property
optimization, implying its high capacity to model data distribution.
Furthermore, MolHF is the first flow-based model that can be applied to model
larger molecules (polymer) with more than 100 heavy atoms. The code and models
are available at this https URL

本研究提出了一种新的层次流模型（MolHF），用于精准设计具有所需特性的大分子结构，该模型通过多尺度架构构建键和每个尺度的粗糙图结构，在难以应用常规模型的情况下实现了非常好的数据分布建模。

MolHF: 分层归一化流应用于分子图生成

MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation

This work introduces a method to tune a sequence-based generative model for
molecular de novo design that through augmented episodic likelihood can learn
to generate structures with certain specified desirable properties. We
demonstrate how this model can execute a range of tasks such as generating
analogues to a query structure and generating compounds predicted to be active
against a biological target. As a proof of principle, the model is first
trained to generate molecules that do not contain sulphur. As a second example,
the model is trained to generate analogues to the drug Celecoxib, a technique
that could be used for scaffold hopping or library expansion starting from a
single molecule. Finally, when tuning the model towards generating compounds
predicted to be active against the dopamine receptor type 2, the model
generates structures of which more than 95% are predicted to be active,
including experimentally confirmed actives that have not been included in
either the generative model nor the activity prediction model.

本文介绍了一种基于序列的生成模型来设计分子的方法，通过增强情节似然性可以学习生成具有某些指定的理想特性的结构。将模型应用于生成类似物质结构，生成与药物 Celecoxib 类似的分子以及具有活性的化合物。最后，将模型调整为生成预测对多巴胺受体 2 具有活性的化合物，得到了 95% 以上的预测为活性的结构，其中包括实验证实的活性化合物。