The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information. Then our model mapped the heterogeneous graph data into a latent vector space by using an encoder and employed a self-regressive generative model as a decoder for molecular generation. Additionally, we performed transfer learning on the model using a small set of ligand molecules known to be active against the target protein to generate molecules that bind better to the target protein. Experimental results demonstrate that our model is highly competitive with state-of-the-art models. It can generate valid and diverse molecules with favorable physicochemical properties and drug-likeness. Importantly, they produce novel molecules with high docking scores against the target proteins.

中文翻译：

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FraHMT：目标蛋白的面向片段的异质图分子生成模型

分子生成任务是计算化学和药物发现领域的关键一步，旨在通过计算生成具有特定性质的分子结构。与之前主要关注SMILES字符串或分子图的模型相比，我们的模型特别强调分子的子结构信息，使模型能够从分子的片段和化学反应信息中学习更丰富的化学规则和结构特征。为了实现这一目标，我们对分子进行碎片化，以基于原子和碎片信息构建异构图表示。然后我们的模型通过使用编码器将异构图数据映射到潜在向量空间，并采用自回归生成模型作为分子生成的解码器。此外，我们使用一小组已知对目标蛋白具有活性的配体分子对模型进行迁移学习，以生成与目标蛋白更好结合的分子。实验结果表明，我们的模型与最先进的模型相比具有很强的竞争力。它可以生成具有良好理化性质和药物相似性的有效且多样化的分子。重要的是，它们产生了与目标蛋白具有高对接分数的新型分子。