ArticleMolecules (Basel, Switzerland)2025
Systematic Exploration of Small-Molecule Binding via a Large Language Model Trained on Textualized Protein-Ligand Interactions.
Article in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
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Who cites it
1 citing paper in PubMed.
- ZBP1 in Neuroinflammation and Neurodegeneration: Z-Nucleic-Acid Sensing, RHIM Signalling and Therapeutic Targeting.International journal of molecular sciences · 2026Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emergent Large Language Models (LLMs) show impressive capabilities in performing a wide range of tasks. These models can be harnessed for biophysical use as well. The main challenge in this endeavor lies in transforming 3D chemical data into 1D language-like data. We developed a method to transform molecular data into language-like data and tokenize it for LLM use in a biophysical context. We then trained a model and validated it with a known protein-ligand complex. Using the pre-trained result, the model can assess the chemical properties of targets, detect shared binding properties and structures, and reveal related drugs. The model and the synthetic language to describe binding interactions uncovered novel protein-protein networks influenced by ligands, indicating functionally related yet previously unreported interactions.
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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.