用扩散模型提升分子生成效率，构建更精准的图变压器框架
JTreeformer: Graph-Transformer via Latent-Diffusion Model for Molecular Generation
- 将分子生成转为枝干树生成，结合GCN与注意力机制编码
- 引入有向无环图结构解码器，逐步合成完整分子结构
- 在编码器潜空间插入扩散模型，显著提升采样效率
基于原始化学分子分布发现新分子对医药研究至关重要。图变压器相比传统图网络在性能和可扩展性上更具优势，已被广泛用于图结构任务。然而现有基于变压器的图解码器难以有效利用图信息，仅依赖节点序列而忽略分子图的复杂拓扑结构。本文提出名为JTreeformer的图变压器框架，将分子生成转化为枝干树生成。编码器采用GCN与多头注意力并行；解码器引入有向无环图结构的图基变压器，通过每一步利用部分构建分子结构的信息，迭代合成完整分子。此外，在编码器生成的潜空间中嵌入扩散模型，以增强采样效率与效果。实验表明，该框架优于现有分子生成方法，为药物发现提供有力工具。
原文摘要 · Abstract (English)
The discovery of new molecules based on the original chemical molecule distributions is of great importance in medicine. The graph transformer, with its advantages of high performance and scalability compared to traditional graph networks, has been widely explored in recent research for applications of graph structures. However, current transformer-based graph decoders struggle to effectively utilize graph information, which limits their capacity to leverage only sequences of nodes rather than the complex topological structures of molecule graphs. This paper focuses on building a graph transformer-based framework for molecular generation, which we call \textbf{JTreeformer} as it transforms graph generation into junction tree generation. It combines GCN parallel with multi-head attention as the encoder. It integrates a directed acyclic GCN into a graph-based Transformer to serve as a decoder, which can iteratively synthesize the entire molecule by leveraging information from the partially constructed molecular structure at each step. In addition, a diffusion model is inserted in the latent space generated by the encoder, to enhance the efficiency and effectiveness of sampling further. The empirical results demonstrate that our novel framework outperforms existing molecule generation methods, thus offering a promising tool to advance drug discovery (https://anonymous.4open.science/r/JTreeformer-C74C).
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