College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China.
The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Republic of Korea.
J Chem Inf Model. 2024 May 13;64(9):3718-3732. doi: 10.1021/acs.jcim.4c00252. Epub 2024 Apr 22.
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.
分子生成任务在计算化学和药物发现领域中具有重要地位，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不同，我们的模型特别关注分子上的子结构信息，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。为此，我们将分子分割成基于原子和片段信息的异构图表示形式。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，并使用自回归生成模型作为解码器进行分子生成。此外，我们使用一小部分已知对靶蛋白具有活性的配体分子对模型进行了迁移学习，以生成与靶蛋白结合更好的分子。实验结果表明，我们的模型具有很强的竞争力，能够生成具有良好理化性质和类药性的有效且多样化的分子。重要的是，它们生成了针对靶蛋白具有高对接分数的新型分子。
