A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction

• Main Authors: Kristy Carpenter, Alexander Pilozzi, Xudong Huang
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Molecules
• Subjects: artificial intelligence (AI); machine learning (ML); deep learning (DL); recurrent neural network (RNN), virtual drug screening
• Online Access: https://www.mdpi.com/1420-3049/25/15/3372

The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound–target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC₅₀<i> </i>of the target compounds.<i> </i>The performance of the models was assessed primarily through analysis of the Q² values produced from runs of differing sample and epoch size; recorded loss values were also reported and graphed. The performance of the models was limited, though multiple changes are proposed for potential improvement of a multi-input recurrent neural network-based screening tool.