The toxicity data of compounds

<b>toxric_30_datasets.zip</b>: The expanded predictive toxicology dataset is sourced from TOXRIC, a comprehensive and standardized toxicology database. The toxric_30_datasets contains 30 assay datasets with ~150,000 measurements related to five categories. These categories span a range of toxicity assessment, including genetic toxicity, organic toxicity, clinical toxicity, developmental and reproductive toxicity, and reactive toxicity. <b>multiple_endpoint_acute_toxicity_dataset.zip</b> &amp; <b>all_descriptors.txt</b>: This 59-endpoint acute toxicity dataset is sourced from TOXRIC. It includes 59 various toxicity endpoints with 80,081 unique compounds represented using SMILES strings, and 122,594 usable toxicity measurements described by continuous values with a unified toxicity chemical unit: -log(mol/kg). The larger the measurement value, the stronger the toxicity intensity of the corresponding compound towards a certain endpoint. The 59 acute toxicity endpoints involve 15 different species including mouse, rat, rabbit, guinea pig, dog, cat, bird wild, quail, duck, chicken, frog, mammal, man, women, and human, 8 different administration routes including intraperitoneal, intravenous, oral, skin, subcutaneous, intramuscular, parenteral, and unreported, and 3 different measurement indicators including LD50 (lethal dose 50%), LDLo (lethal dose low), and TDLo (toxic dose low). In this dataset, each compound only has toxicity measurement values concerning a small number of toxicity endpoints, so this dataset is very sparse with nearly 97.4% of compound-to-endpoint measurements missing. Meanwhile, this dataset is also extremely data-unbalanced with some endpoints having tens of thousands of toxicity measurements available, e.g., mouse-intraperitoneal-LD50 has 36,295 measurements, mouse-oral-LD50 has 23,373 measurements, and rat-oral-LD50 has 10,190 measurements, etc, while some endpoints contain only around 100 measurements like mouse-intravenous-LDLo, rat-intravenous-LDLo, frog-subcutaneous-LD50, and human-oral-TDLo, etc. The sparsity and unbalance of this dataset present acute toxicity evaluation as a challenging issue. Among the 59 endpoints, 21 endpoints with less than 200 measurements were considered small-sized endpoints, and 11 endpoints with more than 1000 measurements were treated as large-sized endpoints. Three endpoints targeting humans, human-oral-TDLo, women-oral-TDLo, and man-oral-TDLo, are typical small-sized endpoints, with only 140, 156, and 163 available toxicity measurements, respectively (The acute toxicity intensity measurement values of the 80,081 compounds concerning 59 acute toxic endpoints, as well as the 5-fold random splits, were provided in the <b>multiple_endpoint_acute_toxicity_dataset.zip</b>. The molecular fingerprints or feature descripors of the 80,081 compounds, such as Avalon, Morgan, and AtomPair, were given in the <b>all_descriptors.txt</b>).<b>115-endpoint_acute_toxiciy_dataset</b><b>.zip</b>: We collected more acute toxicity data of compounds from PubChem database through web crawling. We unified all the toxicity measurement units into -log(mol/kg) and retained the endpoints with no less than 30 available samples per endpoint. Thus, a brand-new acute toxicity dataset containing 115 endpoints was established. Compared with the previous 59-endpoint acute toxicity dataset from TOXRIC, the number of acute toxicity endpoints in this new dataset has doubled, adding more possible species (like goat, monkey, hamster, etc), administration routes (like intracerebral, intratracheal), and measurement indicators (like LD10, LD20). It should be emphasized that the sample imbalance among endpoints and the data missing rate of this dataset are more severe. Its sparsity rate reaches 98.7%, and it contains 68 small-sample acute toxicity endpoints (i.e., endpoints with less than 200 toxicity measurement data), among which the endpoint with the fewest samples has only 30 available measurement data. Therefore, this dataset is more challenging for all current acute toxicity prediction models.