In Silico Prediction of the Toxicity of Nitroaromatic Compounds: Application of Ensemble Learning QSAR Approach.

ABSTRACT: In this work, a dataset of more than 200 nitroaromatic compounds is used to develop Quantitative Structure-Activity Relationship (QSAR) models for the estimation of in vivo toxicity based on 50% lethal dose to rats (LD₅₀). An initial set of 4885 molecular descriptors was generated and applied to build Support Vector Regression (SVR) models. The best two SVR models, SVR_A and SVR_B, were selected to build an Ensemble Model by means of Multiple Linear Regression (MLR). The obtained Ensemble Model showed improved performance over the base SVR models in the training set (R² = 0.88), validation set (R² = 0.95), and true external test set (R² = 0.92). The models were also internally validated by 5-fold cross-validation and Y-scramb

SUBMITTER: Daghighi A 

PROVIDER: S-EPMC9786026 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature