Vascular Endothelial Growth Factor (VEGF) plays a central role in angiogenesis, regulating both physiological processes such as wound healing, tissue repair, and bone formation, and pathological events including tumor progression, metastasis, and diabetic retinopathy. Due to its crucial role in vascular biology, VEGF serves as an important therapeutic target in anti-angiogenic drug development and precision medicine. However, conventional experimental methods for VEGF identification are costly and time-consuming, emphasizing the need for efficient computational approaches. To address this challenge, we introduce DeepStack-VEGF, an advanced deep learning framework designed for accurate and robust VEGF prediction. The model integrates diverse sequence-derived features, including physicochemical descriptors, sequential patterns, evolutionary information, and secondary structure motifs, further enhanced by pretrained embeddings from UniProt and ProtBert. Feature optimization was achieved using Support Vector Machine-Recursive Feature Elimination. DeepStack-VEGF employs a stacking ensemble of three architectures including Feedback Generative Adversarial Network Gated Recurrent Unit and Capsule Convolutional Neural Network each contributing distinct representational capabilities. Comprehensive evaluations demonstrate that the fused feature set and stacking ensemble substantially outperform individual models, achieving superior accuracy, robustness, and generalization. By combining deep learning with biological insight, DeepStack-VEGF provides a reliable and scalable computational framework for VEGF identification, supporting rational drug discovery, anti-angiogenic therapy design, and precision medicine applications.
Ali et al. (Wed,) studied this question.