ArticleJournal of cheminformatics2023
Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- A Multi-modal Drug Target Affinity Prediction Based on Graph Features and Pre-trained Sequence Embeddings.Interdisciplinary sciences, computational life sciences · 2025Article
- Interpretable drug-target affinity prediction based on pre-trained models' output as embeddings and based on structure-aware cross-attention for feature fusion.Molecular diversity · 2025Article
- Identify potential drug candidates within a high-quality compound search space.Briefings in bioinformatics · 2024Article
- T4Seeker: a hybrid model for type IV secretion effectors identification.BMC biology · 2024Article
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model.Journal of cheminformatics · 2024Article
- Integrated convolution and self-attention for improving peptide toxicity prediction.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.
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