Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
<|reference_start|>Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking: Docking is a crucial component in drug discovery aimed at predicting the binding conformation and affinity between small molecules and target proteins. ML-based docking has recently emerged as a prominent approach, outpacing traditional methods like DOCK and AutoDock Vina in handling the growing scale and complexity of molecular libraries. However, the availability of comprehensive and user-friendly datasets for training and benchmarking ML-based docking algorithms remains limited. We introduce Smiles2Dock, an open large-scale multi-task dataset for molecular docking. We created a framework combining P2Rank and AutoDock Vina to dock 1.7 million ligands from the ChEMBL database against 15 AlphaFold proteins, giving us more than 25 million protein-ligand binding scores. The dataset leverages a wide range of high-accuracy AlphaFold protein models, encompasses a diverse set of biologically relevant compounds and enables researchers to benchmark all major approaches for ML-based docking such as Graph, Transformer and CNN-based methods. We also introduce a novel Transformer-based architecture for docking scores prediction and set it as an initial benchmark for our dataset. Our dataset and code are publicly available to support the development of novel ML-based methods for molecular docking to advance scientific research in this field.<|reference_end|>
@article{menestrel2024smiles2dock:,
    title={Smiles2Dock: an open large-scale multi-task dataset for ML-based
  molecular docking},
    author={Thomas Le Menestrel, Manuel Rivas},
    journal={arXiv preprint arXiv:2406.05738},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05738},
    primaryClass={q-bio.BM cs.LG stat.AP stat.CO}
}