ArticleInternational journal of molecular sciences2022
SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- SaeGraphDTI: drug-target interaction prediction based on sequence attribute extraction and graph neural network.BMC bioinformatics · 2025Article
- Enhancing drug-target interaction prediction with graph representation learning and knowledge-based regularization.Frontiers in bioinformatics · 2025Article
- NFSA-DTI: A Novel Drug-Target Interaction Prediction Model Using Neural Fingerprint and Self-Attention Mechanism.International journal of molecular sciences · 2024Article
- Decrypting orphan GPCR drug discovery via multitask learning.Journal of cheminformatics · 2024Article
- The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.PloS one · 2024Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug-target interaction.BMC bioinformatics · 2023Article
- Integrating Multiple Single-Cell RNA Sequencing Datasets Using Adversarial Autoencoders.International journal of molecular sciences · 2023Article
- Molecular Property Prediction by Combining LSTM and GAT.Biomolecules · 2023Article
- Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.International journal of molecular sciences · 2022Article
- NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.International journal of molecular sciences · 2022Article
- Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Pixel-level multimodal fusion deep networks for predicting subcellular organelle localization from label-free live-cell imaging.Frontiers in genetics · 2022Article
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Authors and funding
4 authors.
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Abstract
Identifying compound-protein (drug-target, DTI) interactions (CPI) accurately is a key step in drug discovery. Including virtual screening and drug reuse, it can significantly reduce the time it takes to identify drug candidates and provide patients with timely and effective treatment. Recently, more and more researchers have developed CPI's deep learning model, including feature representation of a 2D molecular graph of a compound using a graph convolutional neural network, but this method loses much important information about the compound. In this paper, we propose a novel three-channel deep learning framework, named SSGraphCPI, for CPI prediction, which is composed of recurrent neural networks with an attentional mechanism and graph convolutional neural network. In our model, the characteristics of compounds are extracted from 1D SMILES string and 2D molecular graph. Using both the 1D SMILES string sequence and the 2D molecular graph can provide both sequential and structural features for CPI predictions. Additionally, we select the 1D CNN module to learn the hidden data patterns in the sequence to mine deeper information. Our model is much more suitable for collecting more effective information of compounds. Experimental results show that our method achieves significant performances with RMSE (Root Mean Square Error) = 2.24 and R2 (degree of linear fitting of the model) = 0.039 on the GPCR (G Protein-Coupled Receptors) dataset, and with RMSE = 2.64 and R2 = 0.018 on the GPCR dataset RMSE, which preforms better than some classical deep learning models, including RNN/GCNN-CNN, GCNNet and GATNet.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.