Chen, Xingmei published the artcileMachine learning-based prediction of toxicity of organic compounds towards fathead minnow, Application In Synthesis of 2447-79-2, the publication is RSC Advances (2020), 10(59), 36174-36180, database is CAplus and MEDLINE.
Predicting the acute toxicity of a large dataset of diverse chems. against fathead minnows (Pimephales promelas) is challenging. In this paper, 963 organic compounds with acute toxicity towards fathead minnows were split into a training set (482 compounds) and a test set (481 compounds) with an approx. ratio of 1 : 1. Only six mol. descriptors were used to establish the quant. structure-activity/toxicity relationship (QSAR/QSTR) model for 96 h pLC50 through a support vector machine (SVM) along with genetic algorithm. The optimal SVM model (R2 = 0.756) was verified using both internal (leave-one-out cross-validation) and external validations. The validation results (qint2 = 0.699 and qext2 = 0.744) were satisfactory in predicting acute toxicity in fathead minnows compared with other models reported in the literature, although our SVM model has only six mol. descriptors and a large data set for the test set consisting of 481 compounds
RSC Advances published new progress about 2447-79-2. 2447-79-2 belongs to amides-buliding-blocks, auxiliary class Chloride,Amine,Benzene,Amide, name is 2,4-Dichlorobenzamide, and the molecular formula is C7H5Cl2NO, Application In Synthesis of 2447-79-2.
Referemce:
https://en.wikipedia.org/wiki/Amide,
Amide – an overview | ScienceDirect Topics