Computational drug-target interaction (DTI) prediction can reduce the cost and time of drug discovery, yet existing approaches require complex preprocessing, specialized molecular representations, or structure-based workflows, limiting rapid and scalable deployment. Convolutional neural networks offer an alternative by learning discriminative patterns directly from 2D image representations, bypassing extensive feature engineering. Here, we present DEEPScreen++, a modular, open-source framework that formulates DTI prediction as an image-based learning task using RDKit-generated 2D molecular images. DEEPScreen++ provides an end-to-end pipeline for automated data curation from ChEMBL, MoleculeNet, and Therapeutic Data Commons (TDC); configurable 36-fold rotational data augmentation; and efficient model training and inference. The framework supports multiple modern backbones, including convolutional neural networks (CNNs), SwinV2 vision transformers (ViTs), and YOLOv11. With optimized training procedures, target-specific models converge rapidly and can be trained within hours on widely available consumer-grade GPUs. Across public benchmarks from TDC and MoleculeNet, DEEPScreen++ achieves competitive performance relative to existing methods. Integrated SHAP- and saliency-based interpretability modules highlight atom-level features driving model predictions. We demonstrate the practical utility of DEEPScreen++ in two independent studies: drug repurposing for monkeypox virus and de novo screening for AKT1 kinase, both of which identify experimentally confirmed active molecules. Designed for reproducible benchmarking and straightforward extension to new targets, DEEPScreen++ requires minimal preprocessing and accessible computational resources, positioning it as a practical alternative to traditional descriptor-based pipelines. The tool is openly available at https://github.com/HUBioDataLab/DEEPScreen2.

Co-authors: Atabey Ünlü, Mehmet Furkan Çalışkan, Furkan Necati İnan, Kemal Örer, Kerem Örer