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Wine Quality
Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).
Wine
Using chemical analysis to determine the origin of wines
Glass Identification
From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)
Similarity Prediction
Molecular similarity assessments by expert chemists. Useful for the prediction of molecular similarity evaluations by humans.
Drug_induced_Autoimmunity_Prediction
This dataset comprises molecular descriptors generated using RDKit, specifically curated for the study of drug-induced autoimmunity through ensemble machine learning approaches. It is divided into a training set and a testing set, containing numerical features that represent molecular properties and structural characteristics of drugs. The dataset supports predictive modeling tasks aimed at identifying potential autoimmune risks associated with drug candidates. These molecular descriptors include physicochemical properties, providing a comprehensive foundation for machine learning analysis. The dataset facilitates the development of interpretable models for drug toxicity prediction, contributing to advancements in computational toxicology and drug safety assessment.
ApisTox
ApisTox is a dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and cheminformatics. Code used to produce the dataset is available at https://github.com/j-adamczyk/apis_tox_dataset
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