Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China.
School of Pharmacy, Lanzhou University, Lanzhou 730000, Gansu, P. R. China.
J Chem Inf Model. 2023 May 22;63(10):2983-2991. doi: 10.1021/acs.jcim.3c00572. Epub 2023 May 10.
A deep generation model, as a novel drug design and discovery tool, shows obvious advantages in generating compounds with novel backbones and has been applied successfully in the field of drug discovery. However, it is still a challenge to generate molecules with expected properties, especially high activity. Here, to obtain compounds both with novelty and high activity to a target, we proposed a conditional molecular generation model COMG by considering the docking score and 3D pharmacophore matching during molecular generation. The proposed model was based on the conditional variational autoencoder architecture constrained by the pharmacophore matching score. During Bayesian optimization, the docking score was applied to enhance the target relevance of generated compounds. Furthermore, to overcome the problem of high structural similarity caused by Bayesian optimization, the idea of the scaffold memory unit was also introduced. The evaluation results of COMG show that our model not only can improve the structural diversity of generated molecules but also can effectively improve the proportion of target-related drug-active molecules. The obtained results indicate that our proposed model COMG is a useful drug design tool.
一种深度生成模型作为一种新型的药物设计和发现工具，在生成具有新颖骨架的化合物方面显示出明显的优势，并已成功应用于药物发现领域。然而，要生成具有预期性质的分子，特别是高活性的分子，仍然是一个挑战。在这里，为了获得对靶标既新颖又具有高活性的化合物，我们在分子生成过程中考虑了对接评分和 3D 药效团匹配，提出了一种条件分子生成模型 COMG。所提出的模型基于受药效团匹配评分约束的条件变分自动编码器架构。在贝叶斯优化过程中，对接评分被用于增强生成化合物的靶标相关性。此外，为了克服贝叶斯优化引起的高结构相似性问题，还引入了支架记忆单元的思想。COMG 的评估结果表明，我们的模型不仅可以提高生成分子的结构多样性，而且可以有效地提高与靶标相关的药物活性分子的比例。所得结果表明，我们提出的 COMG 模型是一种有用的药物设计工具。
