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Abstract：
SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.Scientific contributionThis novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn\'t require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.
摘要：
基于SMILES的生成模型是用于增强药物设计的最强大和成功的最新方法之一。它们通常用于完全从头生成，然而,支架装饰和片段链接应用程序有时是可取的，需要不同的语法，architecture,训练数据集，因此，重新训练新模型。在这项工作中,我们描述了一个简单的过程，用基于SMILES的生成模型进行约束分子生成，通过提供SMILES提示将适用性扩展到支架装饰和片段连接，不需要再培训。结合强化学习，我们展示了预先训练的，仅解码器模型快速适应这些应用，并可以进一步优化分子生成朝着指定的目标。我们将这种方法的性能与各种正交方法进行了比较，并表明性能相当或更好。为方便起见,我们提供了一个易于使用的python包，以促进模型采样，可以在GitHub和Python包索引上找到。科学贡献这种新颖的方法将自回归化学语言模型扩展到支架装饰和片段链接场景。这不需要重新训练,使用定制的语法，或定制数据集的策展，这是其他方法通常需要的。
