Understanding the reasons a compound succeeds or fails during the drug discovery process is a complex problem. Despite numerous approaches to reduce the number of failures, attrition rates and R&D costs in drug discovery remain high . One major obstacle in retrospective analyses of the drug discovery process is the limited availability of high-quality open-source data spanning different stages of the drug discovery pipeline including compound bioactivity data from the preclinical and clinical phases, as well as data about approved drugs. Use of company data is limited to occasional collaborations between pharmaceutical companies 2 and analyses of in-house data. One of the main resources for open-source bioactivity data is ChEMBL . Bioactivity data in ChEMBL covers a broad range of different compounds, bioactivity endpoints, assays, targets, and organisms. In addition, ChEMBL provides data for all stages of the drug discovery process: patent bioactivity data; preclinical compound data from literature and donated by collaborators; data on clinical candidates including information on their highest clinical phase (MAX_PHASE); as well as drug data with annotations to indications and drug warning information. The dataset presented here extracts pairs of interacting compounds and targets from ChEMBL for which there are measured activities, or which are in a table of manually curated disease relevant interactions in ChEMBL (DRUG_MECHANISM table). Various compound and target annotations are added to facilitate analyses of sets of compounds that interact with the same target or a target in the same target class. A similar dataset was curated previously to identify differences in drug-like properties and ligand efficiencies between drugs and comparator compounds binding to the same target . The herein presented work has extended the previous dataset to include clinical candidates and newer ChEMBL data. Furthermore, the dataset can now be generated in a fully reproducible and automated manner for every ChEMBL version from ChEMBL 26 onwards. As with all databases, the data in ChEMBL is not complete. The bioactivity data and related compounds, targets, and assays that people choose to publish are representing certain areas of scientific interest and are often biased towards positive findings . New research might cover other areas of research foci and/or sometimes uncover inaccuracies in earlier scientific findings. We still hope that the automatic generation of the dataset will allow the exploration of the status quo as knowledge advances. The dataset in this study was generated from ChEMBL 32. Table provides an overview of the numbers of compounds, targets, and compound-target pairs for the full dataset and for one of the available subsets (BF_100_c_dt_d_dt) of the dataset. The subset is limited to targets with at least one hundred compounds with a measured activity at that target and at least one drug or clinical candidate that is known to interact with the target. These criteria limit the subset to targets with enough data for which a comparison of drugs and clinical candidates with other compounds is possible, i.e., targets which are particularly interesting for exploring drug discovery-related questions. The number of compound-target pairs and compounds is similar for both the full dataset and the subset. However, the number of targets in the subset is less than half of the number of targets in the full dataset. This implies that the filtering criteria remove a significant number of targets with a small number of compounds from the dataset, respectively. In total, the dataset (subset) comprises 614,594 (583,398) compoundtarget pairs with 5,109 (2,639) drugs and 3,932 (2,619) clinical candidates that are known to interact with the respective target. The dataset contains information about a wide variety of different targets and target classes. The distribution of target classes in the dataset and the BF_100_c_dt_d_dt subset are shown in Figure . In both cases about half of the targets in the dataset are enzymes, with kinases being the most common enzyme class. This is followed by membrane receptors, comprising mostly of family A GPCRs, and making up 16.1% and 25.1% of the full dataset and the subset, respectively. Other noticeable target classes include ion channels and transcription factors which both represent ten percent or less of the targets in the dataset.