Summary of Accurate Prediction Of Ligand-protein Interaction Affinities with Fine-tuned Small Language Models, by Ben Fauber
Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models
by Ben Fauber
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach for predicting the affinity of ligands binding to proteins, a crucial step in drug discovery. The method utilizes fine-tuned generative small language models (SLMs) and only requires the SMILES string of the ligand and the protein’s amino acid sequence as inputs. In a zero-shot setting, the model accurately predicts a range of affinity values for out-of-sample data, surpassing existing machine learning-based methods like FEP+ and traditional free-energy perturbation. The authors’ approach has significant implications for accelerating drug discovery campaigns against challenging therapeutic targets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to predict how well a medicine will bind to a target in the body. That’s exactly what this paper does! Scientists have developed a new way to use computers to make these predictions using special language models. All they need is the chemical structure of the medicine and the protein it might bind to. This new method is better than existing methods at making accurate predictions, which can help us develop medicines faster. |
Keywords
» Artificial intelligence » Machine learning » Zero shot
