This work introduces MiDi, a diffusion model for jointly generating molecular graphs and corresponding 3D conformers. In contrast to existing models, which derive molecular bonds from the conformation using predefined rules, MiDi streamlines the molecule generation process with an end-to-end differentiable model. Experimental results demonstrate the benefits of this approach: on the complex GEOM-DRUGS dataset, our model generates significantly better molecular graphs than 3D-based models and even surpasses specialized algorithms that directly optimize the bond orders for validity. Our code is available at github.com/cvignac/MiDi.

翻译：本文提出了MiDi，一种用于联合生成分子图和相应3D构象的扩散模型。与现有依赖预定义规则从构象推导分子键的模型不同，MiDi通过端到端可微分模型简化了分子生成过程。实验结果表明了该方法的优势：在复杂的GEOM-DRUGS数据集上，我们的模型生成的分子图显著优于基于3D的模型，甚至超过了直接优化键序有效性的专用算法。我们的代码已开源至github.com/cvignac/MiDi。