Deep learning methods applied to chemistry can be used to accelerate the discovery of new molecules. This work introduces GraphINVENT, a platform developed for graph-based molecular design using graph neural networks (GNNs). GraphINVENT uses a tiered deep neural network architecture to probabilistically generate new molecules a single bond at a time. All models implemented in GraphINVENT can quickly learn to build molecules resembling the training set molecules without any explicit programming of chemical rules. The models have been benchmarked using the MOSES distribution-based metrics, showing how GraphINVENT models compare well with state-of-the-art generative models. This work compares six different GNN-based generative models in GraphINVENT, and shows that ultimately the gated-graph neural network performs best against the metrics considered here.

应用于化学的深度学习方法可用于加速新分子的发现。这项工作介绍了GraphINVENT，这是一个为使用图神经网络（GNN）进行基于图的分子设计而开发的平台。GraphINVENT使用分层的深度神经网络架构来一次一次单键概率地产生新分子。在GraphINVENT中实现的所有模型都可以快速学习构建类似于训练集分子的分子，而无需对化学规则进行任何明确的编程。该模型已使用基于MOSES分布的指标进行了基准测试，显示了GraphINVENT模型与最新的生成模型的比较结果。这项工作在GraphINVENT中比较了六个不同的基于GNN的生成模型，