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

• URL: http://arxiv.org/abs/2407.00111v1
• Date: Thu, 27 Jun 2024 13:04:58 GMT
• Title: Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models
• Authors: Ben Fauber,
• Abstract summary: We describe the accurate prediction of ligand-protein interaction (LPI) affinities with instruction fine-tuned pretrained generative small language models (SLMs) Our results demonstrate a clear improvement over machine learning (ML) and free-energy perturbation (FEP+) based methods in accurately predicting a range of LPI affinities.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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