The invention discloses a rare and endangered
organism toxicity prediction method based on a
machine learning
algorithm and a
quantitative structure-activity relationship, which constructs a
toxicity prediction model through the
machine learning
algorithm, can effectively assess the
toxicity influence of environmental pollutants on rare and endangered organisms, and provides
technical support for rare and endangered
organism protection and
ecological environment risk assessment. Comprising the following steps: 1, collecting data related to rare and endangered organisms from a public
database, and establishing a rare and endangered
organism toxicity prediction
database based on
machine learning; 2, generating molecular descriptors for the chemical substances; step 3, data preprocessing; 4, development of acute and chronic toxicity prediction models of rare and endangered organisms based on multiple
machine learning is carried out, and performance evaluation is carried out; 5, performing internal and external
verification on the rare and endangered biotoxicity prediction model; step 6, analyzing the importance of the features by using the valuable, rare and endangered biotoxicity prediction model, and finding out the most influential features; and 7, predicting the toxicity value of the pollutants in combination with the optimal
machine learning model.