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

arXiv: https://arxiv.org/abs/2407.00111 · HTML: https://mlsanity.com/paper/2407.00111/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [TinyStories: How Small Can Language Models Be and Still Speak Coherent English?](https://mlsanity.com/paper/2305.07759/) (2023)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [WideDTA: prediction of drug-target binding affinity](https://mlsanity.com/paper/1902.04166/) (2019)
- [Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks](https://mlsanity.com/paper/2402.05616/) (2024)

## Built on by (most cited first, 2 of 2)

- [Learning the Latent Rules of a Game from Data: A Chess Story](https://mlsanity.com/paper/2410.02426/) (2024)
- [SPADE: Faster Drug Discovery by Learning from Sparse Data](https://mlsanity.com/paper/2605.05370/) (2026)

## Abstract (arXiv)

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.