Generating new molecules is fundamental to advancing critical applications such as drug discovery and material synthesis. Flows can generate molecules effectively by inverting the encoding process, however, existing flow models either require artifactual dequantization or specific node/edge orderings, lack desiderata such as permutation invariance, or induce discrepancy between the encoding and the decoding steps that necessitates post hoc validity correction. We circumvent these issues with novel continuous normalizing E(3)-equivariant flows, based on a system of node ODEs coupled as a graph PDE, that repeatedly reconcile locally toward globally aligned densities. Our models can be cast as message-passing temporal networks, and result in superlative performance on the tasks of density estimation and molecular generation. In particular, our generated samples achieve state-of-the-art on both the standard QM9 and ZINC250K benchmarks.
翻译:生成新分子对于推进药物发现和材料合成等关键应用至关重要。 流动可以通过颠倒编码过程而有效生成分子,然而,现有的流动模型要么需要原生脱序,要么需要特定的节点/尖点定,缺乏脱位,例如变异,或者导致编码与需要临时有效性校正的解码步骤之间的差异。我们绕过这些问题,在节点代码系统以及图PDE的基础上,使E(3)-等质流不断实现新颖的正常化,这种流动又反复地使本地调和到与全球一致的密度。我们的模型可以被铸成信息传递时间网络,在密度估计和分子生成的任务上产生超强性性性能。特别是,我们生成的样本在标准 QM9 和 ZINC250K 基准上都达到了最新水平。