DEP: Dual-Path Embeddings for Protein Toxicity Classification - Gambogi, André; Buzelin, Arthur; Miserani, Gabriela; Evangelista, Guilherme H. G.; Bento, Pedro; Aquino, Yan; Malquias, Samira; Bacelar, Pedro; Dutenhefner, Pedro Robles; Meira Jr., Wagner; Pappa, Gisele L. (Anais do Simpósio Brasileiro de Bioinformática (BSB), 2025)
The reliance of protein predictors on computationally expensive 3D structures or MSAs severely limits large-scale screening. To overcome this limitation, we propose a lightweight, dual-path architecture operating exclusively on 1D sequence embeddings. Our model fuses representations from a Local-Hierarchical Path, designed to capture functional motifs, with a Global-Holistic Path that models long-range dependencies. Evaluated on a benchmark toxicity dataset, our model establishes a new state-of-the-art with an AUC-ROC of 0.966, surpassing complex models that require structural inputs. These results show that a well-designed, sequence-only approach can be a faster, more scalable, and even better-performing alternative to structure-based methods.