论文标题
基于能量的目标特异性药物发现的生成模型
Energy-based Generative Models for Target-specific Drug Discovery
论文作者
论文摘要
由于药物在疾病发病机理中的关键作用，药物靶标是药物发现的主要重点。由于生物分子数据集的可用性增加，计算方法被广泛应用于药物开发。流行的生成方法可以通过学习给定的分子分布来创建新药物分子。但是，这些方法主要不是针对特定目标的药物发现。我们开发了一种基于能量的概率模型，用于计算靶标特异性药物发现。结果表明，我们提出的tagmol可以生成具有与实际分子相似的结合亲和力得分的分子。基于GAT的模型相对于GCN基线模型显示出更快，更好的学习。
Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis. Computational approaches are widely applied to drug development because of the increasing availability of biological molecular datasets. Popular generative approaches can create new drug molecules by learning the given molecule distributions. However, these approaches are mostly not for target-specific drug discovery. We developed an energy-based probabilistic model for computational target-specific drug discovery. Results show that our proposed TagMol can generate molecules with similar binding affinity scores as real molecules. GAT-based models showed faster and better learning relative to GCN baseline models.
