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 find that nearly all of the model's performance can be maintained without any use of global self-attention, showing that message passing is still a competitive approach for 3D molecular property prediction despite the recent dominance of graph transformers. We also find 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。该模型整合了精心调优的局部消息传递组件与有偏的全局注意力机制，并结合先前文献中的关键思想，在大规模分子数据集PCQM4Mv2上实现了最优结果。通过彻底的消融研究，我们揭示了各组件的贡献，并发现即使完全不使用全局自注意力机制，模型几乎仍可保持全部性能，这表明在图Transformer近期主导该领域的背景下，消息传递方法在三维分子性质预测中仍具有竞争力。此外，我们还发现，在缺乏三维位置信息的情况下，我们的方法显著优于现有技术。