可显式控制分子属性的3D药物分子生成新方法
Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
- 解耦潜在空间，分别建模分子属性与结构上下文
- 支持对药效性、可合成性等属性的精准调控
- 适合药物分子设计与靶点结合的逆向药物发现
我们研究在显式控制分子属性（如类药性、可合成性评分）和有效结合特定蛋白位点条件下的3D药物分子生成。为此，提出一种E(3)-等变的Wasserstein自编码器，将生成模型的潜在空间分解为分子属性与剩余3D结构上下文两个解耦部分。模型在保持坐标表示的等变性及数据似然不变性的同时，实现对分子属性的显式控制。此外，引入基于对齐的坐标损失，使等变网络能够从零开始进行自回归的3D分子生成。大量实验验证了该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，适用于从头3D分子设计及针对蛋白靶标的结构药物发现。
原文摘要 · Abstract (English)
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.
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