title: Accurate Prediction of Ligand-Protein Interaction Affinities with
    Fine-Tuned Small Language Models
summary: 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_authors._id: 6683467417b03cbc03628c13
paper_authors.name: Ben Fauber
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-02T00:14:55.683Z
paper_authors._id: 6331bab56eae0bb0a01968ea
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6331bab56eae0bb0a01968ea/DxFrb2wZKFp0mmmdTa4tg.png
paper_authors.fullname: Ben Fauber
paper_authors.type: user
paper_authors.user: BFauber
paper_title: Accurate Prediction of Ligand-Protein Interaction Affinities with
    Fine-Tuned Small Language Models
paper_summary: 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_ai_keywords: ligand-protein interaction (LPI) drug-target interactions (DTI) instruction fine-tuned pretrained generative small language models (SLMs) SMILES string amino acid sequence zero-shot setting free-energy perturbation (FEP+)