Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability. Results: We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 10-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, an attention mechanism is embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions.

利用领域知识和深度学习方法，提出了一种半监督深度学习模型，旨在从蛋白质序列中预测化合物与蛋白质相互作用并对其进行解释，表现优于传统方法，并采用转移学习进一步提高了新型蛋白质的预测效果以及对筛选药物的解释性能。

DeepAffinity: 基于统一的循环卷积神经网络的化合物-蛋白亲和性可解释的深度学习