rzMLP-DTA: gMLP network with ReZero for sequence-based drug-target affinity prediction
Zongzhao Qiu, Qihong Jiao, Yuxiao Wang, Cheng Chen, Daming Zhu, Xuefeng Cui · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
Computational algorithms are being successfully used to speed up drug development processes, primarily by way of turning biochemical problems into data problems. Recently, with increasing amounts of available biological data generated by biochemical methods measured affinity, some computational algorithms based deep learning for predicting drug-target affinity (DTA) become promising directions for accelerating the process of drug development. These deep learning models first attempts focus on representation learning of individual amino acids or atoms. Next global average pooling layers in those models are used to to combine such individual features to global features, and finally simple feed forward networks are adopted to yield affinity predictions. Notably, research which has been undertaken on global feature aggregations (e.g., the global pooling and the feed forward layers) for the drug-target affinity problem is still lacked currently. To address this issue, we propose a new rzMLP block featured newly designed global feature aggregations. This rzMLP block is based on two recent technologies in deep learning research: the gMLP model and the ReZero layer. We use gMLP model to aggregate input features with a constant size, while the ReZero layer is used to smooth the training process of this block. Our rzMLP is capable of learning complicated global features while overcoming the problems caused by the model being too deep. Importantly, when we compared a model contained rzMLP block to others, the mean squared error(MSE) decreases by 33%. Comparing to state-of-the-art methods for predicting affinity, rzMLP-DTA achieves the lowest MSE and highest CI on two benchmarks, Davis and KIBA datasets, respectively.