  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.
