Automatic Quantitative Structure–Activity Relationship Modeling to Fill Data Gaps in High-Throughput Screening

Heather L. Ciallella, Elena Chung, Daniel P. Russo, Hao Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

Advances in high-throughput screening (HTS) revolutionized the environmental and health sciences data landscape. However, new compounds still need to be experimentally synthesized and tested to obtain HTS data, which will still be costly and time-consuming when a large set of new compounds need to be studied against many tests. Quantitative structure–activity relationship (QSAR) modeling is a standard method to fill data gaps for new compounds. The major challenge for many toxicologists, especially those with limited computational backgrounds, is efficiently developing optimized QSAR models for each assay with missing data for certain test compounds. This chapter aims to introduce a freely available and user-friendly QSAR modeling workflow, which trains and optimizes models using five algorithms without the need for a programming background.

Original languageEnglish (US)
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages169-187
Number of pages19
DOIs
StatePublished - 2022
Externally publishedYes

Publication series

NameMethods in Molecular Biology
Volume2474
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics

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