DCUI-MGraphDTA: Enabling Efficient Inference of a Drug-Target Binding Affinity Prediction Model on DCUs

Efficient and accurate identification of drug-target affinity (DTA) is crucial in the virtual screening stage of drug discovery and the reuse of existing drugs. Deep neural networks are becoming popular tools for providing fast and accurate binding affinity prediction. MGraphDTA is one of the best performing models for DTA prediction, utilizing a super-deep graph neural network to extract features, demonstrating good generalization and interpretation capabilities. However, the complex deep neural networks come with a higher computational cost of model inference. Meanwhile, due to the expansion of drug databases, rapid completion of large-scale virtual screening still presents challenges. In this work, we propose an efficient binding affinity prediction method called DCUI-MGraphDTA through a series of optimizations on MGraphDTA to achieve large-scale virtual screening for millions of molecules on the Deep Computing Unit (DCU) cluster. The optimized DCUI-MGraphDTA model achieves a speedup of approximately 5.83 times on a single DCU compared to the original MGraphDTA. As the number of DCUs expands from 1 to 32, the total time for the model to process 1.28 million data significantly decreases, achieving a speedup of approximately 11.9 times.