| _version_ | 1866902218298359808 |
|---|---|
| author | Wang, Xiangyu |
| author_facet | Wang, Xiangyu |
| contents | <p dir="auto">Sequence-based deep learning models for ligand design have recently garnered increasing interests in the research community. These end-to-end approaches, which generate ligand SMILES directly from protein amino acid sequences, not only enable access to a broader and more readily available training dataset but also allow the model to move beyond the constraints of binding pocket structures and leverage the richer information embedded in the primary sequence.</p> <p dir="auto">This project aims to provide an all-in-one, cross-platform, and efficient framework for sequence-based ligand design, streamlining the complex process into a series of simple and user-friendly steps—such as ligand molecule generation, property evaluation, filtering, novelty checking, and docking.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16992819 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DrugGENIUS: An All-in-One Framework for Sequence-based Ligand Design Wang, Xiangyu <p dir="auto">Sequence-based deep learning models for ligand design have recently garnered increasing interests in the research community. These end-to-end approaches, which generate ligand SMILES directly from protein amino acid sequences, not only enable access to a broader and more readily available training dataset but also allow the model to move beyond the constraints of binding pocket structures and leverage the richer information embedded in the primary sequence.</p> <p dir="auto">This project aims to provide an all-in-one, cross-platform, and efficient framework for sequence-based ligand design, streamlining the complex process into a series of simple and user-friendly steps—such as ligand molecule generation, property evaluation, filtering, novelty checking, and docking.</p> |
| title | DrugGENIUS: An All-in-One Framework for Sequence-based Ligand Design |
| url | https://doi.org/10.5281/zenodo.16992819 |