论文标题
概率生成变压器语言模型用于分子生成设计
Probabilistic Generative Transformer Language models for Generative Design of Molecules
论文作者
论文摘要
自我监督的神经语言模型最近在有机分子和蛋白质序列的生成设计中发现了广泛的应用，以及用于下游结构分类和功能预测的表示学习。但是，大多数现有的分子设计深度学习模型通常都需要一个大数据集并具有黑盒架构，这使得很难解释其设计逻辑。在这里，我们提出了生成分子变压器（GMTRANSFORMER），这是一种用于分子生成设计的概率神经网络模型。我们的模型建立在最初用于文本处理的空白填充语言模型上，该模型在学习具有高质量生成，可解释性和数据效率的“分子语法”方面具有独特的优势。与其他基线相比，我们的模型在摩西数据集上以基准为基准，获得了很高的新颖性和SCAF。概率生成步骤具有修补分子设计的潜力，因为它们有能力推荐如何通过解释的解释来修饰现有分子的能力，并在学习的隐式分子化学的指导下。可以在https://github.com/usccolumbia/gmtransformer上自由访问源代码和数据集
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer
