Abstract
Medicinal chemists often optimize drugs considering their 3D structures and designing structurally distinct molecules that retain key features, such as shapes, pharmacophores, or chemical properties. Previous deep learning approaches address this through supervised tasks like molecule inpainting or property-guided optimization. In this work, we propose a flexible zero-shot molecule manipulation method by navigating in a shared latent space of 3D molecules. We introduce a Variational AutoEncoder (VAE) for 3D molecules, named MolFLAE, which learns a fixed-dimensional, SE(3)-equivariant latent space independent of atom counts. MolFLAE encodes 3D molecules using an SE(3)-equivariant neural network into fixed number of latent nodes, distinguished by learned embeddings. The latent space is regularized, and molecular structures are reconstructed via a Bayesian Flow Network (BFN) conditioned on the encoder's latent output. MolFLAE achieves competitive performance on standard unconditional 3D molecule generation benchmarks. Moreover, the latent space of MolFLAE enables zero-shot molecule manipulation, including atom number editing, structure reconstruction, and coordinated latent interpolation for both structure and properties. We further demonstrate our approach on a drug optimization task for the human glucocorticoid receptor, generating molecules with improved hydrophilicity while preserving key interactions, under computational evaluations. These results highlight the flexibility, robustness, and real-world utility of our method, opening new avenues for molecule editing and optimization.
Abstract (translated)
药物化学家在优化药物时，常常会考虑其三维结构，并设计出具有特定关键特征（如形状、药效团或化学性质）的分子。以前的深度学习方法通过监督任务（例如分子修复或属性引导优化）来解决这个问题。在这项工作中，我们提出了一种灵活的零样本分子操作方法，该方法通过在3D分子共享潜在空间中导航实现。为此，我们引入了一个针对三维分子的变分自动编码器（VAE），名为MolFLAE。MolFLAE学习一个与原子计数无关的固定维度、SE(3)等变的潜在空间。 MolFLAE使用SE(3)-等变神经网络将三维分子编码为固定的潜在节点数量，这些节点通过学习嵌入进行区分。该潜在空间受到正则化处理，并且分子结构可以通过贝叶斯流网络（BFN）在给定编码器输出的情况下重建。MolFLAE在标准无条件的3D分子生成基准上实现了具有竞争力的表现。 此外，MolFLAE的潜在空间支持零样本分子操作，包括原子数编辑、结构重构和同时进行结构与属性调整的协调潜在插值。我们进一步展示了这种方法在人类糖皮质激素受体药物优化任务中的应用，通过计算评估证明生成的分子具有改善的亲水性，同时保持了关键相互作用。 这些结果突显了该方法的灵活性、鲁棒性和现实世界的实用性，并为分子编辑和优化开辟了新的途径。
URL
https://arxiv.org/abs/2506.00771
