We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction. Our model integrates a well-tuned local message passing component and biased global attention with other key ideas from prior literature to achieve state-of-the-art results on large-scale molecular dataset PCQM4Mv2. Through a thorough ablation study we highlight the impact of individual components and, contrary to expectations set by recent trends, find that nearly all of the model's performance can be maintained without any use of global self-attention. We also show that our approach is significantly more accurate than prior art when 3D positional information is not available.

翻译：我们提出GPS++，一种用于分子性质预测的混合消息传递神经网络/图Transformer模型。该模型集成了精心调优的局部消息传递组件、有偏全局注意力及先前文献中的其他关键思想，在大型分子数据集PCQM4Mv2上取得了最先进的性能。通过全面的消融研究，我们突显了各组件的影响，并发现与近期趋势的预期相反，几乎无需任何全局自注意力即可维持模型全部性能。我们还证明，在缺乏三维位置信息时，本方法的精度显著优于现有技术。