Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints-Reference-Cited by-同舟云学术

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Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints

Published:2021-04 Issue: Volume:340 Page:4-14
ISSN:0378-4274
Container-title:Toxicology Letters
language:en
Short-container-title:Toxicology Letters

Author:

Feng Huawei,Zhang Li^ORCID,Li Shimeng,Liu Lili,Yang Tianzhou,Yang Pengyu,Zhao Jian,Arkin Isaiah Tuvia,Liu Hongsheng

Funder

China Association for Science and Technology

Department of Education of Liaoning Province

Liaoning University

Department of Science and Technology of Liaoning Province

Publisher

Elsevier BV

Subject

Toxicology,General Medicine

Reference53 articles.

1. Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints;Ai;Toxicol. Sci.,2018

2. An overview of machine learning and big data for drug toxicity evaluation;Andy;Chem. Res. Toxicol.,2020

3. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?;Bajusz;J. Cheminform.,2015

4. Integrated QSAR models to predict acute oral systemic toxicity;Ballabio;Mol. Inform.,2019

5. QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes;Basant;Toxicol. Res.,2016

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1. Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals;Journal of Environmental Science and Health, Part C;2024-09-03

2. Reproductive chemical database: a curated database of chemicals that modulate protein targets regulating important reproductive biological processes;Cell & Bioscience;2024-06-06

3. A QSAR study for predicting malformation in zebrafish embryo;Toxicology Mechanisms and Methods;2024-04-15

4. Optimized Bags of Artificial Neural Networks Can Predict the Viability of Organisms Exposed to Nanoparticles;The Journal of Physical Chemistry A;2024-03-27

5. High-throughput computational screening and molecular fingerprint design of metal-organic framework adsorbents for separation of C3 components;Giant;2024-03

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