Literature DB >> 27311476

QSAR Models at the US FDA/NCTR.

Huixiao Hong1, Minjun Chen2, Hui Wen Ng2, Weida Tong2.   

Abstract

Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.

Entities:  

Keywords:  Databases; Endocrine disruptors; FDA; Liver toxicity

Mesh:

Year:  2016        PMID: 27311476     DOI: 10.1007/978-1-4939-3609-0_18

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  3 in total

1.  Machine Learning Models for Predicting Liver Toxicity.

Authors:  Jie Liu; Wenjing Guo; Sugunadevi Sakkiah; Zuowei Ji; Gokhan Yavas; Wen Zou; Minjun Chen; Weida Tong; Tucker A Patterson; Huixiao Hong
Journal:  Methods Mol Biol       Date:  2022

2.  Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach.

Authors:  Andres Gonzalez-Jimenez; Ayako Suzuki; Minjun Chen; Kristin Ashby; Ismael Alvarez-Alvarez; Raul J Andrade; M Isabel Lucena
Journal:  Arch Toxicol       Date:  2021-03-05       Impact factor: 5.153

3.  QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches.

Authors:  R D Jawarkar; Ravindrakumar L Bakal; Magdi E A Zaki; Sami Al-Hussain; Arabinda Ghosh; Ajaykumar Gandhi; Nobendu Mukerjee; Abdul Samad; Vijay H Masand; Israa Lewaa
Journal:  Arab J Chem       Date:  2021-10-19       Impact factor: 6.212

  3 in total

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