FusionNet- A Hybrid Deep Learning Approach for Accurate Drug-Target Binding Prediction

Abstract

Identifying drug-target binding affinities (DTBA) is crucial in drug discovery, to understand how effectively drugs interact with their targets. However, traditional methods often struggle to accurately capture the complex relationships in biological data, leading to limitations in their predictive power. This paper introduces FusionNet, an advanced deep-learning model designed to improve DTBA prediction. FusionNet combines the strengths of Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Transformers, to better understand both short-range and long-range interactions in biological sequences and employs the Layer-wise Adaptive Moments (LAMB) optimizer, which ensures the model is more efficient and stable, especially when working with large datasets. FusionNet achieved an MSE of 0.20 and an rm2 of 0.681 on the Davis dataset and an MSE of 0.18 and an rm2 of 0.71 on the KIBA dataset, significantly outperforming existing models like SimBoost, GANsDTA, DeepCDA, and DeepDTA, making it a powerful tool for drug discovery and bioinformatics. This work not only enhances the accuracy of DTBA prediction but also sets new performance standards by integrating advanced neural network architectures and optimizing their training process. FusionNet effectively addresses the limitations of previous approaches, offering a more reliable and efficient method for predicting drug-target interactions.