A Mutual Attention Model for Drug Target Binding Affinity Prediction

Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence of interaction. Several machine learning methods have been developed for this purpose. However, almost all depend heavily on the use of increasingly sophisticated inputs to improve their performance. In addition, these methods do not allow any analysis or interpretation due to their black-box characteristic. This work is an attempt to overcome these limitations by taking advantage of the use of attention mechanisms with convolution models. In this paper, we define a new mutual attention based model for DTBA prediction. We represent both compounds and targets by sequences. Our model starts by aligning the drug-target pairs, then a learned masking is performed to retain the most promising regions, of both sequences, and amplify them with a learned factor in such a way to make the learning focus more on them. We evaluate the performance of our method on two benchmark datasets, KIBA and Davis. The results show that our mutual attention approach is very effective. Compared to other well-known approaches, it achieved excellent results regarding the considered performance metrics.