The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.

中文翻译：

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使用图形生成多任务模型弥合基于目标和基于细胞的药物发现之间的差距

新药的开发对于保护人类免受疾病侵害至关重要。在过去的几十年里，靶向筛选一直是最流行的新药开发方法之一。该方法在体外有效地筛选了目标蛋白的潜在抑制剂，但由于所选药物的活性不足，它在体内经常失败。需要准确的计算方法来弥合这一差距。在这里，我们提出了一种新颖的图形多任务深度学习模型来识别具有抑制性和c目标的化合物有源 (MATIC) 属性。在精心策划的 SARS-CoV-2 数据集上，与传统方法相比，所提出的 MATIC 模型在体内筛选有效化合物方面显示出优势。在此之后，我们研究了模型的可解释性，发现目标抑制（体外）或细胞活性（体内）任务的学习特征与分子特性相关性和原子功能注意不同。基于这些发现，我们利用基于蒙特卡罗的强化学习生成模型来生成具有体外和体内功效的新型多性质化合物，从而弥合了基于靶点和基于细胞的药物发现之间的差距。该工具可在 https://github.com/SIAT-code/MATIC 上免费访问。