ArticlePLoS computational biology2024
A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- Information leakage in enzyme-substrate prediction.Bioinformatics (Oxford, England) · 2026Article
- Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- KASSPer: kinase active site structure prediction using protein and ligand language models and its application to virtual screening.Bioinformatics (Oxford, England) · 2026Article
- Low rank adaptation of chemical foundation models generates effective odorant representations.bioRxiv : the preprint server for biology · 2026Article
- Advancing Structure Elucidation with a Flexible Multi-Spectral AI Model.Angewandte Chemie (International ed. in English) · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- DrugForm-TAS: Target-Agnostic Selectivity as Proteome-wide Binding Propensity Estimation.Computational and structural biotechnology journal · 2026Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- DeepMolecules: a web server for predicting enzyme and transporter-small molecule interactions.Nucleic acids research · 2025Article
- SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction.Journal of chemical information and modeling · 2025Article
- Robust enzyme discovery and engineering with deep learning using CataPro.Nature communications · 2025Article
- DrugForm-DTA: Towards real-world drug-target binding affinity model.Computational and structural biotechnology journal · 2025Article
- Topology-Driven Discovery of Transmembrane ProteinbioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.
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.