保持简单：语言模型能够学习复杂的分子分布
Keeping it Simple: Language Models can learn Complex Molecular Distributions
AI总结：
本文研究简单语言模型学习复杂分子分布的能力，通过多个挑战性任务与图生成模型对比，证明语言模型性能更优，能准确生成ZINC15和PubChem中的复杂分子分布。
摘要：
深度生成分子模型已日益流行，在相关数据集上训练后，这些模型被用于搜索化学空间。生成模型在新型功能化合物逆向设计中的下游效用，取决于其学习分子训练分布的能力。最简单的例子是采用循环神经网络形式、利用字符串表示生成分子的语言模型。更复杂的是图生成模型，它们顺序构建分子图，通常能达到最先进的性能。然而，近期研究表明，语言模型的能力远超以往认知，尤其是在低数据场景下。在本工作中，我们探究了简单语言模型学习分子分布的能力。为此，我们通过汇编特别复杂的分子分布，引入了若干具有挑战性的生成建模任务。在每个任务上，我们将语言模型与两种广泛使用的图生成模型进行了能力对比评估。结果表明，语言模型是强大的生成模型，能够熟练地学习复杂的分子分布，并且性能优于图模型。语言模型能够准确生成：ZINC15 中得分最高的惩罚 LogP 分子分布、多模态分子分布以及 PubChem 中最大的分子。
英文摘要：
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compounds depends on their ability to learn a training distribution of molecules. The most simple example is a language model that takes the form of a recurrent neural network and generates molecules using a string representation. More sophisticated are graph generative models, which sequentially construct molecular graphs and typically achieve state of the art results. However, recent work has shown that language models are more capable than once thought, particularly in the low data regime. In this work, we investigate the capacity of simple language models to learn distributions of molecules. For this purpose, we introduce several challenging generative modeling tasks by compiling especially complex distributions of molecules. On each task, we evaluate the ability of language models as compared with two widely used graph generative models. The results demonstrate that language models are powerful generative models, capable of adeptly learning complex molecular distributions -- and yield better performance than the graph models. Language models can accurately generate: distributions of the highest scoring penalized LogP molecules in ZINC15, multi-modal molecular distributions as well as the largest molecules in PubChem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
