论文标题
具有域适应性的可解释的双线性注意网络可改善药物目标预测
Interpretable bilinear attention network with domain adaptation improves drug-target prediction
论文作者
论文摘要
预测药物目标相互作用是药物发现的关键。最近基于深度学习的方法显示出令人鼓舞的表现，但仍有两个挑战：（i）如何明确建模并学习药物与目标之间的局部互动，以更好地预测和解释； （ii）如何在不同分布的新型药物目标对上概括预测性能。在这项工作中，我们提出了Dugban，这是一个深厚的双线性注意网络（BAN）框架，并适应了域的适应性，以明确学习药物和目标之间的临时局部互动，并适应分布数据外的数据。 Drugban在药物分子图和靶蛋白序列上进行预测，并具有条件结构域对抗性学习，以使跨不同分布的学习相互作用表示，以更好地对新型药物目标对进行更好的概括。在内域和跨域设置下的三个基准数据集上的实验表明，对于五个最先进的基准，Dugban取得了最佳的总体表现。此外，可视化学习的双线性注意力图提供了可解释的见解，从预测结果中提供了可解释的见解。
Predicting drug-target interaction is key for drug discovery. Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution. In this work, we propose DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pair-wise local interactions between drugs and targets, and adapt on out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug-target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baselines. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.
