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。