GSToxi: Gated Cross-Modal Modeling With Graph-Sequence Encoders for Peptide Toxicity Prediction

Peptide-based therapeutics hold great potential, yet their cytotoxicity remains a key challenge in drug development. Most existing toxicity prediction models rely solely on sequence information, often overlooking the fusion of multimodal submolecular patterns. We propose GSToxi, a multimodal deep learning framework that leverage sequence and molecular graph featurizer to enhance peptide toxicity prediction. A shared gating mechanism is employed to facilitate semantic alignment and cross-modal integration, while a contrastive regularization loss further optimizes latent-space consistency throughout the training process. Furthermore, GSToxi incorporates embeddings from pre-trained protein language models alongside low-level compositional priors, enabling the capture of both global contextual semantics and local structural features. Experimental results show that GSToxi outperforms state-of-the-art baselines across multiple evaluation metrics on an independent test set. Ablation studies underscore the critical contributions of each component, with the molecular graph encoder and pre-trained embeddings proving particularly impactful. This work offers a generalizable and robust framework for peptide toxicity prediction and provides valuable insights for future multimodal modeling of biological molecules.