Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909234267947008 |
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| author | Fauber, Ben |
| author_facet | Fauber, Ben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00111 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models Fauber, Ben Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Accurate Prediction of Ligand-Protein Interaction Affinities with Fine-Tuned Small Language Models |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2407.00111 |