Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models

BF

Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models: 5 upvotes on Hugging Face Daily Papers, #24 of 27 papers on 2024-07-02. Day-by-day upvote history.

We describe the accurate prediction of ligand-protein interaction (LPI) affinities, also known as drug-target interactions (DTI), with instruction fine-tuned pretrained generative small language models (SLMs). We achieved accurate predictions for a range of affinity values associated with ligand-protein interactions on out-of-sample data in a zero-shot setting. Only the SMILES string of the ligand and the amino acid sequence of the protein were used as the model inputs. Our results demonstrate a clear improvement over machine learning (ML) and free-energy perturbation (FEP+) based methods in accurately predicting a range of ligand-protein interaction affinities, which can be leveraged to further accelerate drug discovery campaigns against challenging therapeutic targets.

Paper page on Hugging Face · arXiv

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