ArticleProceedings of the National Academy of Sciences of the United States of America2023
Contrastive learning in protein language space predicts interactions between drugs and protein targets.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
100 citing papers in PubMed, 1 synthesis or guideline pooled it, 168 citations in OpenAlex.
- Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.Frontiers in pharmacology · 2026Pooled it
- Article
- Deciphering the comprehensive relationship between 5' UTR and 3' UTR sequences with deep learning.Bioinformatics (Oxford, England) · 2026Article
- RingKin: portraying the vast macrocyclic chemical universe surrounding kinase drugs.Chemical science · 2026Article
- Contrastive learning unites sequence and structure in a global representation of protein space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Risk-Aware Computational Prioritization and Validation Route Design for Medicine-Food Homology Plant Compounds in a Parkinson's Disease Context.Biotech (Basel (Switzerland)) · 2026Article
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Structure-agnostic protein-ligand binding affinity prediction via hierarchical representation alignment.Bioinformatics (Oxford, England) · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.PLoS computational biology · 2026Article
- Evaluation of deep learning architectures for predicting ligand interactions with neurologically relevant GPCRs.Journal of cheminformatics · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- Hierarchical Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms.International journal of molecular sciences · 2026Article
- DeepAden: an explainable machine learning method for predicting the substrate specificity of nonribosomal peptide synthetases.Nucleic acids research · 2026Article
- Unveiling the bioactive landscape of drug inactive ingredients (DIGs) using deep transfer learning.Acta pharmaceutica Sinica. B · 2026Article
- Article
- Machine Learning and Experimental Verification Identify Anti-Influenza Natural Products.International journal of molecular sciences · 2026Article
- CLASPP: A unified model for predicting post-translational modifications.bioRxiv : the preprint server for biology · 2026Article
- A membrane-permeable small molecule biosensor accesses intractable cells and animals without genetic manipulation.bioRxiv : the preprint server for biology · 2026Article
- DrugBLIP: exploring the protein-molecule interaction mechanisms with a multi-task learning graph transformer.Bioinformatics (Oxford, England) · 2026Article
- Identifying microbial protease allergens through protein language model-guided homology.Cell systems · 2026Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (
Indexed as
Identifiers
What OpenQuestion holds
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.