Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints.

ABSTRACT:

Background

Drug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints.

Results

In this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-nega

SUBMITTER: Kim E 

PROVIDER: S-EPMC5471939 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature