{"url":"/dataset/imdrug","name":"ImDrug","full_name":null,"description_markdown":"**ImDrug** is a comprehensive benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It features modularized components including formulation of learning setting and tasks, dataset curation, standardized evaluation, and baseline algorithms. It also provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline such as molecular modeling, drug-target interaction and retrosynthesis.","description_withheld":null,"homepage":"https://github.com/DrugLT/ImDrug","introduced_date":"2022-09-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/imdrug-a-benchmark-for-deep-imbalanced","title":"ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery","first_author":"Lanqing Li","url":null},"license":{"name":"MIT license","url":"https://github.com/DrugLT/ImDrug/blob/main/LICENSE"},"modalities":[{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Drug Discovery","url":"/task/drug-discovery","datasets_with_task":"/datasets/task/drug-discovery"},{"name":"Fairness","url":"/task/fairness","datasets_with_task":"/datasets/task/fairness"}],"languages":[],"variants":["ImDrug"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}