用简单模型生成更符合物理规律的分子，速度提升10倍。
TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality
- 用非等变变换器处理原子序列，事后确定性重建键
- 在GEOM-Drugs上达到最佳构象有效性，推理快10倍
- 无需硬编码对称性仍自发具备旋转等变性，适合药物设计
当前最先进的3D分子生成模型依赖显著归纳偏置，如SE(3)等变性、排列等变性及图消息传递网络以捕捉局部化学特性，但生成分子仍常缺乏物理合理性。我们提出TABASCO，该模型摒弃这些假设：采用标准非等变变换器架构，将分子中原子视为序列，并在生成后确定性重建化学键。由于不使用等变层和消息传递，模型结构大幅简化，数据吞吐量显著提升。在GEOM-Drugs基准上，TABASCO实现最优的PoseBusters有效性，且推理速度比最强基线快约10倍。尽管未显式编码对称性，模型仍表现出自发的旋转等变性。本工作为训练简约、高通量的生成模型提供了范例，适用于基于结构或药效团的药物设计任务。代码已开源至github.com/carlosinator/tabasco。
原文摘要 · Abstract (English)
State-of-the-art models for 3D molecular generation are based on significant inductive biases, SE(3), permutation equivariance to respect symmetry and graph message-passing networks to capture local chemistry, yet the generated molecules still struggle with physical plausibility. We introduce TABASCO which relaxes these assumptions: The model has a standard non-equivariant transformer architecture, treats atoms in a molecule as sequences and reconstructs bonds deterministically after generation. The absence of equivariant layers and message passing allows us to significantly simplify the model architecture and scale data throughput. On the GEOM-Drugs benchmark TABASCO achieves state-of-the-art PoseBusters validity and delivers inference roughly 10x faster than the strongest baseline, while exhibiting emergent rotational equivariance despite symmetry not being hard-coded. Our work offers a blueprint for training minimalist, high-throughput generative models suited to specialised tasks such as structure- and pharmacophore-based drug design. We provide a link to our implementation at github.com/carlosinator/tabasco.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。