Multimodal Deep Learning for Generating Potential Anti-Dengue Peptides.

Dengue virus remains a significant global health threat, imposing a substantial disease burden on nearly half of the world's population. The urgent need for effective antiviral therapeutics, including therapeutic peptides targeting the Dengue virus, is critical in the current healthcare landscape. However, the availability of anti-Dengue peptides (ADPs) data remains limited in existing data sets, posing a challenge for computational modeling and discovery. This study presents a novel multimodal framework integrating high-performance predictive modeling with generative learning to accurately predict and potentially identify novel potent ADPs. Specifically, a predictive model was constructed using a multimodal combination of bidirectional long short-term memory (BiLSTM) and a stacking ensemble of neural networks, both using diverse sequence representations. Additionally, a Wasserstein generative adversarial network with a gradient penalty was employed to generate novel ADP candidates. The predictive models demonstrated robust performance, achieving balanced accuracy, area under the receiver operating characteristic curve, and area under the precision-recall curve exceeding 90%, with a Matthews correlation coefficient surpassing 80%. In addition, glycine (G), phenylalanine (F), and tryptophan (W) are the most influential residues to the inhibitory potency of ADPs. Through the proposed multimodal framework, 33 novel ADP sequences with the highest predictive probabilities were identified. Furthermore, regression analysis using a random forest model was developed to predict three candidate peptides with predicted IC