ArticleBioinformatics (Oxford, England)2023
Perceiver CPI: a nested cross-attention network for compound-protein interaction prediction.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions.bioRxiv : the preprint server for biology · 2026Article
- GraphTransDTA: Drug-Target Affinity Prediction with Graph Transformer for Multimodal Data Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- Chemical genomics language model toward reliable and explainable compound-protein interaction exploration.Journal of cheminformatics · 2026Article
- Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations.Communications chemistry · 2025Article
- A Domain Adaptive Interpretable Substructure-Aware Graph Attention Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- DistRMI: a deep distance-aware neural network for explainable RNA loop motif-small molecule interaction prediction.Briefings in bioinformatics · 2025Article
- ArcDFI: Attention regularization guided by CYP450 interactions for predicting drug-food interactions.PLoS computational biology · 2025Article
- Deep learning-based radiolabelled compound-protein interaction prediction for NDUFS1-targeting radiopharmaceutical discovery.EJNMMI research · 2025Article
- Identifying novel therapeutic targets of natural compounds in traditional Chinese medicine herbs with hypergraph representation learning.Briefings in bioinformatics · 2025Article
- Mapping protein-metabolite interactions iniScience · 2025Article
- MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism.BMC bioinformatics · 2025Article
- ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction.Journal of cheminformatics · 2025Article
- How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Drug-Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset.Journal of chemical information and modeling · 2024Article
- BindingSiteDTI: differential-scale binding site modelling for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2024Article
- Molecular property prediction based on graph structure learning.Bioinformatics (Oxford, England) · 2024Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationCompound-protein interaction (CPI) plays an essential role in drug discovery and is performed via expensive molecular docking simulations. Many artificial intelligence-based approaches have been proposed in this regard. Recently, two types of models have accomplished promising results in exploiting molecular information: graph convolutional neural networks that construct a learned molecular representation from a graph structure (atoms and bonds), and neural networks that can be applied to compute on descriptors or fingerprints of molecules. However, the superiority of one method over the other is yet to be determined. Modern studies have endeavored to aggregate information that is extracted from compounds and proteins to form the CPI task. Nonetheless, these approaches have used a simple concatenation to combine them, which cannot fully capture the interaction between such information.
resultsWe propose the Perceiver CPI network, which adopts a cross-attention mechanism to improve the learning ability of the representation of drug and target interactions and exploits the rich information obtained from extended-connectivity fingerprints to improve the performance. We evaluated Perceiver CPI on three main datasets, Davis, KIBA and Metz, to compare the performance of our proposed model with that of state-of-the-art methods. The proposed method achieved satisfactory performance and exhibited significant improvements over previous approaches in all experiments. AVAILABILITY AND IMPLEMENTATION: Perceiver CPI is available at https://github.com/dmis-lab/PerceiverCPI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.