We consider the conditional generation of 3D drug-like molecules with \textit{explicit control} over molecular properties such as drug-like properties (e.g., Quantitative Estimate of Druglikeness or Synthetic Accessibility score) and effectively binding to specific protein sites. To tackle this problem, we propose an E(3)-equivariant Wasserstein autoencoder and factorize the latent space of our generative model into two disentangled aspects: molecular properties and the remaining structural context of 3D molecules. Our model ensures explicit control over these molecular attributes while maintaining equivariance of coordinate representation and invariance of data likelihood. Furthermore, we introduce a novel alignment-based coordinate loss to adapt equivariant networks for auto-regressive de-novo 3D molecule generation from scratch. Extensive experiments validate our model's effectiveness on property-guided and context-guided molecule generation, both for de-novo 3D molecule design and structure-based drug discovery against protein targets.

翻译：本文研究具有显式控制的三维类药物分子的条件生成问题，控制属性包括类药性质（如定量评估类药性得分或合成可及性得分）以及有效结合特定蛋白质位点的能力。为解决此问题，我们提出一种E(3)等变Wasserstein自编码器，并将生成模型的潜在空间分解为两个解耦的维度：分子属性与三维分子的剩余结构上下文。该模型在保持坐标表示等变性与数据似然不变性的同时，确保了对这些分子属性的显式控制。此外，我们提出一种新颖的基于对齐的坐标损失函数，使等变网络能够适应自回归式的从头三维分子生成任务。大量实验验证了本模型在属性引导与上下文引导的分子生成任务中的有效性，涵盖从头三维分子设计以及针对蛋白质靶点的基于结构的药物发现。