论文标题
Moltrans：用于药物靶标相互作用预测的分子相互作用变压器
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction
论文作者
论文摘要
药物靶标相互作用（DTI）预测是硅药物发现中的一项基础任务，由于需要在大型药物化合物空间上进行实验搜索，因此昂贵且耗时。近年来，在DTI预测中深入学习取得了令人鼓舞的进步。但是，以下挑战仍然是开放的：（1）唯一的数据驱动的分子表示学习方法忽略了DTI的子结构性质，因此产生的结果不太准确且难以解释； （2）现有方法集中在有限的标记数据上，同时忽略了大量未标记分子数据的值。我们提出了一个分子相互作用变压器（MOLTRANS），通过以下方式解决这些局限性：（1）知识启发的次级结构模式挖掘算法和相互作用建模模块，以更准确，可解释的DTI预测； （2）增强的变压器编码器，以更好地提取和捕获从大量未标记的生物医学数据中提取的子结构之间的语义关系。我们在现实世界数据上评估了Moltrans，并显示与最新基准相比，它改善了DTI预测性能。
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space. Recent years have witnessed promising progress for deep learning in DTI predictions. However, the following challenges are still open: (1) the sole data-driven molecular representation learning approaches ignore the sub-structural nature of DTI, thus produce results that are less accurate and difficult to explain; (2) existing methods focus on limited labeled data while ignoring the value of massive unlabelled molecular data. We propose a Molecular Interaction Transformer (MolTrans) to address these limitations via: (1) knowledge inspired sub-structural pattern mining algorithm and interaction modeling module for more accurate and interpretable DTI prediction; (2) an augmented transformer encoder to better extract and capture the semantic relations among substructures extracted from massive unlabeled biomedical data. We evaluate MolTrans on real world data and show it improved DTI prediction performance compared to state-of-the-art baselines.
