Accurately identifying transcription factor (TF) non-binding regions is critical for improving the fidelity of TF binding site (TFBS) predictions. We propose a specialized modeling approach that focuses solely on characterizing unequivocally non-bound genomic segments. By leveraging ChIP-Seq peak data from ENCODE and strictly defining non-binding regions at substantial genomic distances from known TFBS, we aim to construct a deep learning-based classifier. This model delineates non-binding genomic features, offering a refined negative set that can subsequently improve TFBS prediction models.