ArticleNature machine intelligence2024
Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Topology-Aware Deep Learning on Higher-Order Structures for Drug Response Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- Topological deep learning for drug-target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review.Briefings in bioinformatics · 2026Review
- Topological metrics as evolutionary and dynamical descriptors of conformational landscapes within protein families.PLoS computational biology · 2026Article
- Nanoparticle-mediated targeting chimeras transform targeted protein degradation.Nature nanotechnology · 2026Review
- Topological data analysis and topological deep learning beyond persistent homology: a review.Artificial intelligence review · 2026Article
- A unified time-frequency foundation model for sleep decoding.Nature communications · 2026Article
- Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real-World Data.Advanced intelligent discovery · 2025Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Overcoming colorectal cancer and cancer stem cell resistance with photodynamic therapy: new frontiers in oncology.RSC advances · 2025Review
- Drug Resistance Predictions Based on a Directed Flag Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Nanotherapeutic strategies exploiting biological traits of cancer stem cells.Bioactive materials · 2025Review
- CAML: Commutative Algebra Machine Learning─A Case Study on Protein-Ligand Binding Affinity Prediction.Journal of chemical information and modeling · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- A review of transformer models in drug discovery and beyond.Journal of pharmaceutical analysis · 2025Review
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Geometry based prediction of tau protein sites and motifs associated with misfolding and aggregation.Scientific reports · 2025Article
- Category-specific topological learning of metal-organic frameworks.Journal of materials chemistry. A · 2025Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
Abstract
Despite the success of pretrained natural language processing (NLP) models in various fields, their application in computational biology has been hindered by their reliance on biological sequences, which ignores vital three-dimensional (3D) structural information incompatible with the sequential architecture of NLP models. Here we present a topological transformer (TopoFormer), which is built by integrating NLP models and a multiscale topology technique, the persistent topological hyperdigraph Laplacian (PTHL), which systematically converts intricate 3D protein-ligand complexes at various spatial scales into an NLP-admissible sequence of topological invariants and homotopic shapes. PTHL systematically transforms intricate 3D protein-ligand complexes into NLP-compatible sequences of topological invariants and shapes, capturing essential interactions across spatial scales. TopoFormer gives rise to exemplary scoring accuracy and excellent performance in ranking, docking and screening tasks in several benchmark datasets. This approach can be utilized to convert general high-dimensional structured data into NLP-compatible sequences, paving the way for broader NLP based research.
Identifiers
What Socratic 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 Socratic graph.