Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking
This provides a valuable resource for researchers in drug discovery to benchmark and develop ML-based docking methods, though it is incremental as it builds on existing tools and data.
The authors tackled the lack of comprehensive datasets for machine learning-based molecular docking by introducing Smiles2Dock, an open large-scale multi-task dataset with over 25 million protein-ligand binding scores derived from 1.7 million ligands and 15 AlphaFold proteins.
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.