T-pGNN4DTI: Towards better drug-target interactions prediction using Global Self-attentive Pooled Graph Convolutional Networks and protein pre-training Models
A DTI prediction method based on the global self-attentive pooled graph neural network and protein pretraining model, called T-pGNN4DTI, which uses a global self-attention pooled graph neural network to learn more meaningful features of the drug molecule.
Abstract
Identification of drug-target interactions (DTI) is an important and challenging task in drug discovery and development. Traditional methods generally require biological experiments, which are costly and time-consuming. Machine learning-based methods can rapidly predict DTI using only computer algorithmic models, allowing researchers to validate only the most promising interactions through biochemical experiments. This holds promise for effectively addressing the current challenges of lengthy development cycles and high costs in new drug development. However, it is difficult for the existing DTI prediction methods to learn complete and effective feature information from the compound and protein. Therefore, this work proposes a DTI prediction method based on the global self-attentive pooled graph neural network and protein pretraining model, called T-pGNN4DTI. On the one hand, T-pGNN4DTI uses a global self-attention pooled graph neural network to learn more meaningful features of the drug molecule by paying more attention to the information features of certain important atomic nodes of the molecular structure and ignoring some weakly relevant node information features. On the other hand, T-pGNN4DTI uses a pre-trained Transformer-based model to capture the semantic relationships of contexts in long sequences of proteins, which can learn more complete feature information. The results of comparing experiments on three benchmark datasets show that the performance of the proposed T-pGNN4DTI model is better than that of the existing DTI prediction methods, effectively improving the DTI prediction. It provides a new way of thinking to help solve the DTI-related problems.
Drug-target interaction (DTI) prediction is an efficient pre-screening method that uses algorithmic models to assess the binding potential of drug molecules to protein targets. Current research is accelerating towards the integration of heterogeneous graph neural networks, protein language models, and generative artificial intelligence. This review systematically summarizes the latest developments in these technologies, pointing out the problems currently being addressed in research such as data sparsity and cold start, as well as the manifestations of general machine learning challenges such as recommendation systems and noise learning in the biomedical field; Interpret representative models such as Dual Heterogeneous Graph Transformer for Drug-Target Interaction (DHGT-DTI) and Graph Positional encoding and Sequence features for Drug-Target Interaction (GPS-DTI). The combination of dual perspective learning, equivariant graph convolution, and attention mechanism enhances the understanding and reasoning ability of these models in complex biological networks. The generative artificial intelligence diffusion model has opened up a path for developing new drugs through structured and data enhanced approaches. Research has shown that important issues related to computational performance, interpretability, and data compatibility still need to be addressed in existing models. Building a high-performance, multifunctional pre trained model for large-scale biomolecules should be a key direction for development.
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