Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models
<|reference_start|>Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models: 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.<|reference_end|>
@article{fauber2024accurate,
    title={Accurate Prediction of Ligand-Protein Interaction Affinities with
  Fine-Tuned Small Language Models},
    author={Ben Fauber},
    journal={arXiv preprint arXiv:2407.00111},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00111},
    primaryClass={cs.LG cs.AI cs.CL}
}