A-considerable-number-of-benchmark-datasets-support-the-development-of-the-entire-field-of-drug-discovery-and-benchmark-datasets-designed-for-machine-learning-methods-shine-brightly-in-the-galaxy-MoleculeNet-is-a-large-scale-benchmark-dataset-for-mol

The field of drug discovery benefits greatly from a wealth of benchmark datasets, and machine learning methods are particularly well-served by them. Among them, MoleculeNet stands out as a comprehensive benchmark dataset for molecular machine learning, featuring diverse molecular property prediction datasets, various data splits, and evaluation metrics. Additionally, it offers multiple molecular property prediction algorithms via the DeepChem open-source library. Another valuable addition to the collection is the Few-Shot Learning Dataset of Molecules (FS-Mol), which is specifically designed to evaluate few-shot learning methods for predicting molecule activity against a given target protein, known as Quantitative Structure-Activity Relationships (QSAR).