From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization.

ABSTRACT: The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC₅₀ values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training

SUBMITTER: Samanipour S 

PROVIDER: S-EPMC10666547 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature