MOTIVATION: Target discovery is crucial in drug development, especially for complex chronic diseases. Recent advances in high-throughput technologies and the explosion of biomedical data have highlighted the potential of computational druggability prediction methods. However, most current methods rely on sequence-based features with machine learning, which often face challenges related to hand-crafted features, reproducibility, and accessibility. Moreover, the potential of raw sequence and protein structure has not been fully investigated. RESULTS: Here, we leveraged both protein sequence and structure using deep learning techniques, revealing that protein sequence, especially pre-trained embeddings, is more informative than protein structure. Next, we developed DrugTar, a high-performance deep learning algorithm integrating sequence embeddings from the ESM-2 pre-trained protein language model with gene ontologies to predict druggability. DrugTar achieved areas under the curve and precision-recall curve values of 0.94, outperforming state-of-the-art methods. In conclusion, DrugTar streamlines target discovery as a bottleneck in developing novel therapeutics. AVAILABILITY AND IMPLEMENTATION: DrugTar is available as a web server at www.DrugTar.com. The data and source code are at https://github.com/NBorhani/DrugTar.

BACKGROUND: The field of computational drug design is undergoing rapid advancements, highlighting the need for innovative methods to enhance the efficiency and accuracy of calculating ligand-receptor interactions. In this context, we introduce the M01 tool, a comprehensive computational package designed to facilitate the generation and docking of small molecule-peptide hybrids. M01 integrates several established tools, such as RDKit and EasyDock, into a user-friendly platform that automates the workflow from hybrid generation to docking simulations. This tool is particularly beneficial for researchers with limited chemistry expertise, helping them leverage advanced computational techniques. RESULTS: The M01 tool features an intuitive interface for visualizing molecules and selecting connection points in generating new ligands. It also offers automated receptor preparation using UniProt or PDB IDs and generates default docking configuration files. Furthermore, it includes ligand preparation and docking capabilities through EasyDock and calculates molecular descriptors relevant to drug-likeness properties. Validation studies with peptide-alkoxyamine hybrids demonstrated the tool's effectiveness, generating over 14,000 unique hybrid molecules and showcasing its versatility in drug design applications. CONCLUSIONS: The M01 tool represents a significant advancement in computational drug design, streamlining the process of creating hybrid molecules and conducting docking studies. Its ability to automate complex workflows and provide essential molecular insights can empower researchers and enhance the development of novel therapeutics, ultimately contributing to more efficient drug discovery efforts.