Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery

Abstract

The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. The anatomical therapeutic chemical (ATC) classification system, proposed by the World Health Organization (WHO), is an essential source of information for drug repurposing and discovery. Besides, computational methods are applied to predict drug ATC classification. We conducted a systematic review of ATC computational prediction studies and revealed the differences in data sets, data representation, algorithm approaches, and evaluation metrics. We then proposed a deep fusion learning (DFL) framework to optimize the ATC prediction model, namely DeepATC. The methods based on graph convolutional network, inferring biological network and multimodel attentive fusion network were applied in DeepATC to extract the molecular topological information and low-dimensional representation from the molecular graph and heterogeneous biological networks. The results indicated that DeepATC achieved superior model performance with area under the curve (AUC) value at 0.968. Furthermore, the DFL framework was performed for the transcriptome data–based ATC prediction, as well as another independent task that is significantly relevant to drug discovery, namely drug–target interaction. The DFL-based model achieved excellent performance in the above-extended validation task, suggesting that the idea of aggregating the heterogeneous biological network and node’s (molecule or protein) self-topological features will bring inspiration for broader drug repurposing and discovery research.

Multimodal deep learning for biomedical data fusion: a review

“…The authors also successfully extended this fusion strategy to pan-cancer survival prediction [ 80 ]. In addition to element-wise aggregation, attention-based fusion methods can be applied in order to weight different latent features by their importance [ 74 , 79 , 81 ].…”

Protocol for: A Simple, Accessible, Literature-based Drug Repurposing Pipeline

“…Machine learning techniques have gained prominence in drug repositioning and repurposing (For reviews see Chen et al, 2018;Zong et al, 2022;Koshechkin et al, 2022;Yang et al, 2022;Dara et al, 2022;Carracedo-Reboredo et al, 2021;Stephenson et al, 2019;Wang et al, 2024;Ghandikota & Jegga, 2024;Pan et al, 2022).…”

Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy

“…29 To accelerate the drug repurposing and discovery research, Wang et al presented a deep fusion anatomical therapeutic chemical (ATC) prediction model DeepATC, where GCN was used to extract drug topological information. 30 Pham et al developed a mechanism-driven neural network-based architecture DeepCE by incorporating GNN and multihead attention mechanism to support virtual screening of phenotype compounds. Interestingly, they utilized DeepCE to the drug repurposing of COVID-19 and discovered valuable lead drugs consistent with clinical evidence.…”

“…Guo et al developed a general MPNN-based framework coupling with global attention to predict cocrystal density and further identified significant atoms to realize the interpretability of the model . To accelerate the drug repurposing and discovery research, Wang et al presented a deep fusion anatomical therapeutic chemical (ATC) prediction model DeepATC, where GCN was used to extract drug topological information . Pham et al developed a mechanism-driven neural network-based architecture DeepCE by incorporating GNN and multihead attention mechanism to support virtual screening of phenotype compounds.…”