Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.

ABSTRACT:

Background

Chemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost.

Methods

In current studies, QSAR models were developed for hepatotoxicity predictions using the ensemble strategy to integrate machine learning (ML) and deep learning (DL) algorithms using various molecular features. A large dataset of 2588 chemicals and drugs was randomly divided into training (80%) and test (20%) sets, followed by the training of individual base models using diverse machine learni

SUBMITTER: Khan MZI 

PROVIDER: S-EPMC11373136 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature