ArticleJournal of chemical information and modeling2025
Bioactivity Deep Learning for Complex Structure-Free Compound-Protein Interaction Prediction.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- PRGNet: a Parallel Residual Graph Network for enhanced drug-target binding affinity prediction.BMC genomics · 2026Article
- Protein and ligand novelty in drug-target interaction prediction: a dual-encoder fusion strategy for more interpretable and generalizable modeling.BMC bioinformatics · 2026Article
- Article
- Structure-free drug-target affinity prediction using protein and molecule language models.Journal of cheminformatics · 2026Article
- Rethinking network analysis in ethnopharmacology: a multi-omics and AI roadmap to overcome conceptual and methodological biases.Frontiers in pharmacology · 2026Article
- Molecular Pharmacology at the Crossroads of Precision Medicine.Current issues in molecular biology · 2025Article
- Enhancing Transthyretin Binding Affinity Prediction with a Consensus Model: Insights from the Tox24 Challenge.Chemical research in toxicology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Protein-ligand binding affinity assessment plays a pivotal role in virtual drug screening, yet conventional data-driven approaches rely heavily on limited protein-ligand crystal structures. Structure-free compound-protein interaction (CPI) methods have emerged as competitive alternatives, leveraging extensive bioactivity data to serve as more robust scoring functions. However, these methods often overlook two critical challenges that affect data efficiency and modeling accuracy: the heterogeneity of bioactivity data due to differences in bioassay measurements and the presence of activity cliffs (ACs)─small chemical modifications that lead to significant changes in bioactivity, which have not been thoroughly investigated in CPI modeling. To address these challenges, we present CPI2M, a large-scale CPI benchmark data set containing approximately 2 million bioactivity data points across four activity types (
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Registered trials
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.