论文标题
用图生成多任务模型弥合基于目标的基于目标和基于细胞的药物发现之间的差距
Bridging the gap between target-based and cell-based drug discovery with a graph generative multi-task model
论文作者
论文摘要
药物发现对于保护人免受疾病至关重要。基于目标的筛查是过去几十年来开发新药的最流行方法之一。该方法有效地筛选了候选药物，从而在体外抑制靶蛋白，但是由于所选药物在体内的活性不足，通常会失败。需要精确的计算方法来弥合此差距。在这里，我们提出了一个新的图形多任务深度学习模型，以识别具有目标抑制性和细胞活性（matic）特性的化合物。在经过精心策划的SARS-COV-2数据集中，提出的Matic模型显示了与传统方法相比在体内筛选有效化合物的优点。接下来，我们探讨了模型的解释性，发现目标抑制（体外）或细胞活性（体内）任务的学识渊博特征与分子特性相关性和原子功能关注不同。基于这些发现，我们利用了基于蒙特卡洛的增强学习生成模型来生成具有体外和体内功效的新型多型化合物，从而弥合了基于靶标和基于细胞的药物发现之间的差距。
Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the proposed MATIC model shows advantages comparing with traditional method in screening effective compounds in vivo. Next, we explored the model interpretability and found that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attentions. Based on these findings, we utilized a monte carlo based reinforcement learning generative model to generate novel multi-property compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery.
