药效团
计算机科学
人工智能
机器学习
药物发现
贝叶斯网络
数量结构-活动关系
化学
生物信息学
立体化学
生物
作者
Yuwei Yang,Chang‐Yu Hsieh,Yu Kang,Tingjun Hou,Huanxiang Liu,Xiaojun Yao
标识
DOI:10.1021/acs.jcim.3c00572
摘要
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.
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