ProMol_Func: A Structure-Free Deep Learning Model for Virtual Screening.

In computational-aided drug discovery, structure-based drug design models are computationally intensive and rely on protein structures, limiting their scalability and generalization. Additionally, many existing models suffer from inflated false-positive rates due to the scarcity of negative binding data for training. To overcome these challenges, we present ProMol_Func, a structure-free deep learning framework that integrates graph-based encodings of small molecules with protein function embeddings derived solely from amino acid sequences. By augmenting the training data set with both experimentally validated inactives and randomly selected decoys, ProMol_Func improves screening power and generalization. The model achieves state-of-the-art performance on the challenging LIT-PCBA (Library of Integrated Targeted-Panel of Cell-Based Assays) benchmark, with an enrichment factor (EF1%) of 10.9, demonstrating robust screening power in realistic assay settings. Furthermore, in a zero-shot prospective application to