PLMTox: Low-Rank Adaptation on Protein Language Models for Peptide Toxicity Prediction

Peptides, as biologically active substances between small molecules and proteins, play a crucial role in drug development due to their high specificity. However, peptides may trigger toxic reactions when exerting a therapeutic effect. Recently, deep learning has emerged as a leading paradigm for peptide toxicity prediction. With the development of protein language models (PLMs), their advantages in protein sequence analysis provide a new technological path for peptide toxicity prediction, but their practical application is limited by the high computational resource requirements. To address the above problems, this paper proposes a peptide toxicity prediction model, PLMTox, that combines PLMs and low-rank adaptation (LoRA) technology. We use protein language models to learn evolutionary information and deep semantic features behind peptide sequences. Simultaneously, PLMTox employs low-rank matrix decomposition to retain the understanding ability of the pre-trained model while realizing the efficient task adaptation of parameters, thus reducing the demand for computational resources. For different datasets, PLMTox (AUC$=0.963$,$\text{SP}=0.960$) exhibits good performance and robustness. These experiment results suggest that PLMTox is expected to promote the safety assessment process of peptide drug development.